discussion 4

For the week’s topics of Conduct Problems and ADHD, analyze the primary arguments presented in either one of additional articles posted on Canvas OR a relevant empirical, peer-reviewed article of your choosing.

Discuss how the author’s perspective contributes to the broader academic conversation on these subjects. Reflect on the strengths and limitations of the author’s arguments, providing specific examples from the text. Include your critical evaluation of the evidence presented and how it supports or contradicts other sources you have encountered or your current knowledge of the study of abnormal child psychology. Ensure you properly cite (APA formatting, 7th edition) the additional articles from Canvas in your discussion.

Feel free to let me know if you need any more assistance.

Vol.:(0123456789)1 3

Research on Child and Adolescent Psychopathology
https://doi.org/10.1007/s10802-020-00713-9

Inhibitory Control Deficits in Children with Oppositional Defiant
Disorder and Conduct Disorder Compared to Attention Deficit/
Hyperactivity Disorder: A Systematic Review and Meta‑analysis

Mikaela D. Bonham1 · Dianne C. Shanley1 · Allison M. Waters1 · Olivia M. Elvin1

Accepted: 24 September 2020
© Springer Science+Business Media, LLC, part of Springer Nature 2020

Abstract
Inhibitory control decits are known to be characteristic of Oppositional Deant Disorder (ODD), Conduct Disorder (CD),
and Attention-Decit/Hyperactivity Disorder (ADHD); but it is unclear whether children with ODD/CD have inhibitory
control problems independent of ADHD comorbidity. Previous reviews of inhibitory control and ODD/CD have only
focused on one type of measure of inhibitory control or used non-clinical samples. The current meta-analysis explored
inhibitory control problems of children with ODD/CD by systematically reviewing studies where children have a diagnosis
of ODD and/or CD. Comparisons were made across 25 studies between children with ODD/CD, ODD/CD + ADHD, ADHD,
and healthy controls (HC) on various measures of inhibitory control and ADHD symptomatology to explore impacts of
ADHD comorbidity. A small signicant eect (g = -0.58, p < .001) suggested children with ODD/CD are likely to have more diculties with inhibitory control than healthy children. However, comparisons between clinical groups suggested this eect may be due to ADHD symptomatology present in each group. As diculties with inhibitory control are similar, across clinical groups, a dimensional approach to understanding ODD/CD and ADHD may be more useful to consider in future diagnostic criteria. Similarities across clinical groups highlight that therapeutic approaches that assist children with disruptive behaviours could benet from teaching children and their families how to cope with inhibitory control decits. Keywords Inhibitory control · Conduct disorder · Oppositional deant disorder · Executive function · Disruptive behaviour Introduction Children with disruptive behaviour disorders have diculty regulating emotions and inhibiting undesirable behaviours. Executive function plays an important role in the regulation of thoughts, emotions, and behaviours (Diamond 2013). Recently, empirical studies have highlighted the unique role that executive function decits can play in the aetiology of disruptive behaviours (Ezpeleta and Granero 2014; Hobson et al. 2011). Historically, these neurobiological factors have often been overlooked in favour of psychological and social factors that cause disruptive behaviour. According to the Diagnostic and Statistical Manual (DSM-5), disruptive behaviour disorders are classied as Disruptive, Impulse- Control, and Conduct Disorders (DICCD); including conduct disorder (CD), oppositional deant disorder (ODD), kleptomania, intermittent explosive disorder, pyromania, and other or unspecied disruptive, impulse-control and conduct disorders (American Psychological Association 2013). In the past, Attention Decit/Hyperactivity Disorder (ADHD) was classied as a disruptive behaviour disorder, and as such, much of the research on the relationship between executive function deficits and disruptive behaviours has focussed on children with ADHD. However, with the introduction of the DSM-5, ADHD has been reclassified. It is now a Neurodevelopmental Disorder due to empirical evidence that neurobiological deficits (e.g., executive dysfunction) are core characteristics of ADHD (Oosterlaan et al. 1998; Thorell and Wahlstedt 2006; Senderecka et al. 2012). Interestingly, similar deficits are present in early childhood for children with ODD/CD (Schoemaker et al. 2012), but these disorders have remained in the DICCD chapter. This systematic review uses a meta-analytic * Mikaela D. Bonham mikaela.bonham@grithuni.edu.au 1 School of Applied Psychology, Menzies Health Institute of Queensland, Grith University, Mt Gravatt, Quensland 4122, Australia Research on Child and Adolescent Psychopathology 1 3 approach to examine the role of inhibition control, one of the three key components of executive function, in ODD/ CD. It compares the following groups on measures of inhibitory control as well as ADHD symptomatology: ODD/ CD, ODD/CD + ADHD, ADHD, and healthy controls (HC). Systematically reviewing the available empirical evidence will help us to consider whether ODD and CD are better captured within diagnostic manuals as a neurodevelopmental disorder. Aetiology of ODD and CD Psychopathology across the l i fespan typical ly develops from the interaction between individual and environmental factors over time (Matthys and Lochman 2017). When considering the aetiology and treatment of disruptive behaviours, learned behaviour and parenting style have received much attention. Children are exposed to disruptive models of behaviour and learn this behaviour from their environment, which in turn develops into a disruptive behavioural disorder over time (Tremblay 2010). This has been demonstrated in children who model behaviour after coercive parents or associate with delinquent peers when there is an absence of positive parenting (Matthys et al. 2012). When disruptive behaviour disorders are explained by coercive parenting or peer inf luences, interventions naturally follow a “learning-based” approach (Matthys et al., 2012, p. 235). Interventions focussed on parenting and behaviour modification for antisocial youths have demonstrated small to moderate effect sizes; with a mean effect size of 0.47 (range -0.06 to 1.68) and 0.35 (range -1.04 to 1.87), respectively (McCart et al. 2006). Similarly, parenting group interventions for externalising behaviours have also demonstrated small to moderate effect sizes; with a mean effect size of -0.38 favouring intervention (range -0.56 to -0.19; Buchanan-Pascall, Gray, Gordon & Melvin, 2018). Matthy and Lochman (2017) argue that although there is demonstrated effectiveness for behavioural parent training, the effect sizes remain small to moderate, which may be due to many studies being conducted in highly controlled environments which may not be representative of real-world practice. Further, there may also be an impact on children’s ability to learn and problem solve due to neurocognitve impairments in areas such as executive function (Matthys and Lochman, 2017; Matthys et al., 2012). Matthys and colleagues (2012) highlighted that the neurocognitive basis of skill deficits in children with ODD and CD is understudied and understanding its role in the development and maintenance of these disorders has important implications for intervention, with investigation into the role of executive function in ODD/CD as being an important next step. Understanding children’s neurocognitive challenges would be useful to inform more individualised treatment for children and their families (Matthys and Lochman, 2017). This review will be the first to meta-analyse inhibitory control deficits across childhood, in a clinical sample of children with ODD/CD relative to healthy and clinical controls. Inhibitory Control. Executive function deficits are a key characteristic of ADHD. Inhibitory control is one of the three established domains of executive function (Miyake et al. 2000). Broadly, inhibitory control refers to the ability to withhold an emotional or behavioural response in order to achieve a goal (Best and Miller 2010; Nigg 2000; van Goozen et al. 2004). When there is a skill deficit in this area, children have more difficulty stopping unwanted behaviour. While this is the definition used in the present review, across the literature, there are several ways of conceptualising inhibitory control; definitions tend to differ based on the function of inhibitory control. For example, some have conceptualised inhibitory control as executive, motivational, and attentional inhibitory control (Nigg 2000) or prepotent response inhibition, resistance to distractor interference, and resistance to proactive interference (Friedman and Miyake 2004). The current review will examine cool and hot inhibitory control separately because inhibitory control may operate differently based on the emotional salience of the task at hand (Zelazo and Carlson 2012; Zelazo et al. 2010). Therefore, inhibitory control may operate as a ‘hot’ function when a person is in affective contexts, where cues for reward or punishment are present; for example, a delayed snack task employing delayed gratification (Zelazo et al. 2010). On the other hand, ‘cool’ inhibitory control is utilised when presented with abstract problems (Zelazo et al. 2010); for example, a go/ no-go behavioural task, assessing a person’s ability to not respond to a stimulus. Fundamentally, hot and cool executive function require different cognitive processes to be executed (Zelazo and Carlson 2012), which suggests that tasks assessing hot and cool inhibitory control should be analysed separately. Measuring Inhibitory Control. Performance measures and rating scales will be examined separately throughout the review. Accurate measurement of executive function, including inhibitory control is difficult due to the overlapping nature of brain functions. It is impossible to obtain a pure measure of one cognitive process, such as inhibitory control, as all behaviours require more than one cognitive process (Anderson 2002). This issue is known as task impurity, where an outcome from a task or measurement does not solely reect a single ability, because completing the task requires more than one brain function to be executed (Miyake and Friedman 2012). For example, Research on Child and Adolescent Psychopathology 1 3 the Stroop task is a measure to assess inhibitory control however the task requires reading and comprehension to be completed. If an individual is impaired in either of these areas, it may aect their performance on the task overall. Performance measures of inhibitory control are standardised or experimental tasks that capture a child’s ability to inhibit a response when completing a task. Results of performance measures of executive function are often confounded by noise, as other executive and non-executive functions are contributing to performance (Miyake et al. 2000; Miyake and Friedman 2012). Ratng scales oer an ecological way to assess inhibitory control by documenting informant- or self- report of inhibitory control behaviours. They too continue to be aected by task impurity. Further complicating the measurement of inhibitory control, performance tasks and rating scales have been demonstrated by some researchers to not reect the same construct (Bodnar et al. 2007; Toplak et al. 2013). Meta-analysis may allow for a way to assess overall inhibitory control performance at a group level, through a pooled eect size across all measures, providing a more global picture of inhibitory control in children with ODD/ CD. Analysing each task within inhibitory control separately makes it dicult to ascertain an overall eect of inhibitory control. However, understanding differences between performance on each task of inhibitory control is important. Prior meta-analyses of children with ADHD and ODD/CD have only reviewed the Stop Task (Lipszyc and Schachar 2010; Oosterlaan et al. 1998). As such, the analyses in the current review will pool all measures (i.e., according to cool vs. hot, and performance measures vs. rating scales) to understand inhibitory control in children with ODD/CD more globally, as well as subgroup analyses by task. The Relationship Between ODD/CD, ADHD, and Inhibitory Control This review will explore the similarities and dierences between children with ODD/CD, ADHD, ODD/ CD + ADHD, and healthy controls on measures of inhibitory control and ADHD symptomatology. When compared to their typically developing peers, pre-schoolers with ADHD, hard to manage behaviours, and aggressive behaviours have been found to have more diculties with inhibitory control (Schoemaker et al. 2013). However, there were too few studies to conduct ADHD and disruptive behaviour analyses separately. Oosterlaan and colleagues (1998) identied that children with CD (and no ADHD) had more diculties with inhibitory control compared to typically developing children; however, the review was limited to one measure of inhibitory control (i.e., stop signal task). Similarly, Lipszyc and Schachar (2010) meta-analysed performance on the stop signal task, revealing children with ODD/CD, ADHD, and ADHD + ODD/CD had worse performance on the stop signal task compared to healthy controls. Greatest eects were found in children with ADHD, followed by ODD/CD and ADHD + ODD/CD respectively. Similarly, others have identied inhibitory control decits for children with ADHD compared to healthy controls (e.g., Wright, Lipszyc, Dupuis, Thayapararajah, Sathees, and Schachar 2014). There is an absence of reviews where performance on measures of hot inhibitory control has been assessed or reported for children with ODD/CD. Often, behavioural/response inhibition (i.e., cool inhibitory control) is investigated in relation to externalising behaviours such as ODD/CD. Poor response inhibition is thought to be one factor that contributes to externalising behaviour diculties; including conduct problems (Miyake and Friedman 2012). As a result, children with diculties in response inhibition would be expected to have more diculty stopping unwanted behaviours (Hwang et al. 2016). Others have identied more diculties with hot inhibitory control, due to dysfunctional brain circuitry (cortico-striato- thalamo-corticial neurocircuitry) responsible for emotional executive function (Zhu et al. 2018). A review of imaging studies indicated abnormalities in brain regions associated with hot executive functions were more common in children with ODD/CD compared to those with ADHD (Rubia 2011). Further, diculties with emotional responding have been observed in children aged 10 to 18 years, based on fMRI data on an aective stop signal task (Hwang et al. 2016). Despite the inhibitory control decit hypothesis for children with ODD/CD, with a dearth of meta-analytic reviews, the literature lacks consensus as to whether these decits are characteristic of children with ODD/CD. Taken together, our current understanding of inhibitory control deficits in children with ODD/CD is limited; and this is further complicated by the relationship between ODD/CD and ADHD. ADHD has often been found to be comorbid in girls with CD (OR > 40) and ODD (OR = 79), and boys with
CD (OR = 3.7) and ODD (OR = 8.7; Costello et al. 2003).
Other studies have also identied high comorbidity between
CD and ADHD comorbidity in a community sample of
children (OR = 10.7Angold et al. 1999). As ODD/CD
and ADHD are often comorbid, it is dicult to ascertain
whether decits in inhibitory control are due to the severity
of disruptive behaviour in ODD/CD, or whether they may
simply be due to sub-clinical levels of ADHD symptoms
(Blair et al. 2018). For example, youths aged 10 to 18 years
with conduct disorder were found to have more diculties
with hot inhibitory control, however this was attributed to
the presence of ADHD symptoms, rather than the severity
of conduct problems (Hwang et al. 2016). Understanding
whether inhibitory control decits are indeed characteristic
of children with ODD/CD or if they are simply a result

Research on Child and Adolescent Psychopathology

1 3

of ADHD symptomatology may help us understand if
ODD/CD and ADHD are categorically dierent or similar
psychopathology within the same dimension.

Aims

The aim of this review was to comprehensively assess
whether children and adolescents (3–17 years) with a
clinical diagnosis of ODD and/or CD demonstrate inhibitory
control decits more than healthy peers, independent of
ADHD comorbidity. ODD/CD and ADHD are signicantly
comorbid, hence there is a need to examine whether
inhibitory control is similar or dierent for these diagnostic
categories. Therefore, this paper aims to determine whether
children with ODD/CD have greater or lesser inhibitory
control decits when compared to children with ADHD or
healthy controls (HC). We sought to answer these questions
by making comparisons between ODD/CD, ADHD, ODD/
CD + ADHD, and HC groups on measures of inhibitory
control and ADHD symptomatology. Additionally, we
explored whether there were differences between the
aforementioned groups on measures of cool and hot
inhibitory control, rating scales, and the implications of
measuring inhibitory control. All measures of inhibitory
control and ADHD symptomatology were included in
the review. Determining if inhibitory control decits are
characteristic of children with disruptive disorders will
contribute to a more comprehensive aetiological framework,
which in turn will inform more focussed intervention
strategies.

Methods

Inclusion and Exclusion Criteria

Studies were included if: (1) they were written in English,
(2) sample participants were 3-17 years old, (3) sample
participants had a clinical diagnosis of ODD, CD, and/or
comorbid ADHD based on ICD-10, DSM-IV, DSM-IV-TR,
or DSM-5 criteria using either clinical interviewing or
diagnostic measures, (4) outcome measures specically
tested for inhibitory control using a performance or rating
scale, and (5) they had a healthy control (HC) or ADHD
group as a comparison. Studies were excluded if sample
participants had intellectual impairment, Autism Spectrum
Disorder, or other cognitive impairments as comorbid
disorders.

Search Strategy and Study Selection

The meta-analysis followed the recommendations and
standards set by the Preferred Reporting Items for Systematic

Reviews and Meta-Analyses (PRISMA; Moher et al. 2009).
A review protocol was registered with PROSPERO prior
to completion of title and abstract screening. The initial
protocol was amended, and all changes were reected on
the published PROSPERO protocol (CRD42019121527). To
obtain relevant literature, the following electronic databases
were accessed in February 2020: PsycINFO, PubMed,
Embase, CINAHL and Scopus. The nal title and abstract
searches were conducted using the strings:

(executive function OR cognitive function OR executive
dysfunction OR dysexecutive syndrome OR cognitive
dysfunction OR executive control OR executive
impairment OR inhibition OR inhibitory control OR
attentional control OR emotional control OR cognitive
control OR effortful control) AND (externalising
behaviour OR externalizing behavior OR oppositional
defiant disorder OR disruptive behaviour OR
disruptive behavior OR problem behaviour OR
problem behaviour OR conduct problems OR conduct
disorder OR ADHD OR AD/HD OR attention decit
hyperactivity disorder OR hyperkinetic) AND (child
OR children OR adolescent OR kid OR school OR
preschool OR pre-school OR pediatric OR paediatric
OR teen OR teenager OR youth OR boy OR girl).

Key words and MESH terms were determined to be
ineective in this search, as many records that were found
explored executive function as a secondary construct of
interest. Therefore, only title and abstract searches were
used. Searches did not employ a restriction of year of
publication. Reference list checks of review articles (Lipszyc
and Schachar 2010; Oosterlaan et al. 1998; Schoemaker
et al. 2013) and articles included in the current review were
hand-checked to ensure all possible eligible studies were
included.

Data Extraction

A total of 10,622 articles were retrieved through database
and reference list searches. Following deduplication and
assessment for eligibility, a total of 25 studies remained
for nal review. Screening and appraisal were completed
by two independent reviewers (MB and OE). Data
extraction was completed by MB and with cross checks
completed by another reviewer (OE). The following
variables were extracted from the data: sample size, age,
gender composition, country, IQ, primary diagnosis,
comorbid diagnoses, medication status, details of diagnostic
assessment, measures of inhibitory control, definition
of inhibitory control, dependent variable as a measure of
inhibitory control, measures of ADHD symptoms, eect
size, and confounding variables. Where a consensus could
not be reached, a third reviewer was consulted (DS). The

Research on Child and Adolescent Psychopathology

1 3

PRISMA ow diagram outlines assessment of studies in
Fig. 1.

Results

Studies Included

The following meta-analysis and synthesis will address
differences between a healthy control (HC) group and
children with a diagnosis of ODD/CD, ODD/CD + ADHD,
or ADHD. In total, 25 studies were considered for meta-
analysis (see Table 1), with the total included population

ranging from 3 to 14 years of age. Across the included
studies, eight dierent task paradigms were employed to
assess cool inhibitory, one hot inhibitory control measure
was used, and one type of rating scale. When reporting
ADHD symptoms, papers utilised standardised measures,
symptom scales, and symptom counts from diagnostic
interview schedules. Rating scales were either reported as a
T-score, subscale raw score, or an average item score.

Publication Bias and Quality Appraisal

Publication bias was assessed through visual inspection
of the funnel plot of standard error in Hedges G, revealing

Records idenfied
through database

searching February 2020
(n = 10, 619)

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(n = 99)

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Non-clinical sample ODD/CD (n = 14)
DSM-III (n= 10)
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Studies included in
qualitave synthesis

(n=25)

Studies included in
quantave synthesis

(meta-analysis)
(n = 25)

Fig. 1 PRISMA ow chart of studies included in the review

Research on Child and Adolescent Psychopathology

1 3

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lex
ia

Ch
ild

B
eh

av
io

Ch
ec

kl
ist

–
Pa

re
nt

R
ep

or
t:

At
ten

tio
n

Pr
ob

lem
s S

ca
le.

po
rte

d
as

sc
or

Fr
ee

m
eth

yl
ph

en
id

ate

at
lea

st
48

hr
s

pr
io

r t
o t

es
tin

Cu
ed

C
on

tin
uo

Pe
rfo

rm
an

tas
k C

PT
–

A–
X;

M
ea

su
re

re
sp

on
se

hi
bi

tio
n a

m
ot

or
in

hi
bi

tio
n

(in
ter

ch
an

ge
ab

re
fer

re
d t

o a
s

im
pu

lsi
vi

ty
).

ea
su

re
d

us
in

g
nu

m
be

r
of

re
sp

on
se

s t
o

no
n-

tar
ge

ts
at

cu
e

(“A
-n

ot
-X

)

Research on Child and Adolescent Psychopathology

1 3

Ta
bl

e
1

(c
on

tin
ue

Re
fer

en
ce

Su
bj

ec
ts

Ag
e a

M
(S

D)
Se

x
Di

ag
no

sti
c

cr
ite

ria
an

d
as

se
ssm

en
t

Re
po

rte
d

m
or

bi
d

ag
no

se
s

AD
HD

S
ym

pt
om

ea
su

re
M

ed
ica

tio
n

sta
tu

s
Ta

ch
ar

ac
ter

ist
ics

Bo
rk

ow
sk

a e
t a

l.
(2

01
6)

HC
=

47
OD

D
=

21
AD

HD
=

19
HF

A
=

NR 9.6
7(

1.1
1)

9.3
7(

1.5
3)

9.0
5(

1.5
3)

NR
NR

; C
hi

ld
re

n w
er

e
di

ag
no

se
d p

rio
r

to
th

e s
tu

dy
by

a
ch

ild
ps

yc
hi

atr
ist

ne
ur

ol
og

ist
, i

n
co

nj
un

cti
on

w
ith

a p

sy
ch

ol
og

ist
or

he
r s

pe
cia

lis
t

No
co

m
or

bi
di

tie
s

No
t r

ep
or

ted
No

t r
ep

or
ted

M
OX

O-
CP

T;
T

o
id

en
tif

y d
e

cit
s

in
in

hi
bi

tio
n.

Re
po

rte
d

a m
ea

su
re

o
f

im
pu

lsi
ve

ne
ss

;
th

e n
um

be
r o

f
in

ap
pr

op
ria

te
re

sp
on

se
s t

o n
on

–
tar

ge
ts

Ez
pe

let
a a

Gr
an

er
o (

20
15

)
HC

=
53

8
OD

D
=

51
OD

D
+

AD
HD

=
10

AD
HD

=
23

3.7
6(

0.3
3)

3.8
7(

0.3
0)

3.6
9(

0.3
1)

3.7
4(

0.3
3)

26
0 M

29
M

4 M 17
M

DS
M

-IV
-T

Di
ag

no
sti

c
In

ter
vi

ew
fo

r
Ch

ild
re

n a
nd

ol
es

ce
nt

s
fo

r P
ar

en
ts

of
P

re
sc

ho
ol

ild
re

n (
DI

CA
–

PP
YC

)

To
tal

sa
m

pl
e:

AD
HD

, O
DD

,
CD

, d
ep

re
ss

io
n,

pa
ra

tio
n

an
xi

ety
, s

pe
ci

c
ph

ob
ia,

so
cia

l
ph

ob
ia

St
re

ng
th

s a
nd


cu

lti
es

es
tio

nn
air

e:
AD

HD
S

ca
le

(P
ar

en
t a

Te
ac

he
r R

ep
or

t).

Re
po

rte
d

as
ra

w
sc

or
es

No
t r

ep
or

ted
Be

ha
vi

ou
r R

ati
ng

ve
nt

or
y o

f
Ex

ec
ut

iv
e F

un
cti

pr
es

ch
oo

l v
er

sio
n

(B
RI

EF
-P

);
In

hi
bi

t
sc

ale
us

ed
to

se
ss

in
hi

bi
to

co
nt

ro
l

Ki
dd

ie-
Co

nt
in

uo
us

rfo
rm

an
ce

T
as

k
(K

-C
PT

);
m

ea
su

re
s

re
sp

on
se

in
hi

bi
tio

n
th

ro
ug

h n
um

be
r o

f
co

m
m

iss
io

ns
Gl

en
n e

t a
l.

( 2
01

7)
CD

=
32

AD
HD

+
C

D
=

32
AD

HD
=

32
Ns

re
po

rte
d h

er
e a

th
os

e r
ep

or
ted

an
aly

sis

11
.44

(2
.05

)
11

.13
(1

.79
)

11
.03

(1
.89

)

87
.9%

M
89

.5%
M

85
.9%

DS
M

-IV
;

Di
ag

no
sti

c
In

ter
vi

Sc
he

du
le

fo
r C

hi
ld

re
n

(C
-D

IS
C)

No
t r

ep
or

ted
Ch

ild
B

eh
av

io
ur

ec
kl

ist

– A
tte

nt
io

n
su

bs
ca

le.

Re
po

rte
d

as
a

raw
sc

or
e

Co
nn

er
s’

Pa
re

Ra
tin

g S
ca

le
Re

vi
se

d:
S

ho
rt

Fo
rm

–
AD

in
de

x.
Re

po
rte

d
as

ra
w

sc
or

60
.3%

of
th

e
sa

m
pl

e w
er

e
tak

in
g

sti
m

ul
an

t
m

ed
ica

tio
n

St
op

S
ig

na
lT

as
k;

ea
su

re
of

sp
on

se
in

hi
bi

tio
n

an
d i

m
pu

lse

co
nt

ro
l t

hr
ou

SS
RT

e (a
n e

sti
m

ate

of
th

e l
en

gt
h o

f
tim

e b
etw

ee
n

th
e g

o
an

d s
to

p
sti

m
ul

i a
t w

hi
ch

e p
ar

tic
ip

an
t i

s
ab

le
to

in
hi

bi
t t

he
ir

re
sp

on
se

on
50

of
tr

ial
s)

Research on Child and Adolescent Psychopathology

1 3

Ta
bl

e
1

(c
on

tin
ue

Re
fer

en
ce

Su
bj

ec
ts

Ag
e a

M
(S

D)
Se

x
Di

ag
no

sti
c

cr
ite

ria
an

d
as

se
ssm

en
t

Re
po

rte
d

m
or

bi
d

ag
no

se
s

AD
HD

S
ym

pt
om

ea
su

re
M

ed
ica

tio
n

sta
tu

s
Ta

ch
ar

ac
ter

ist
ics

Gu
nt

he
r e

t a
l.

( 2
00

6)
HC

=
23

AD
HD

+
D

BD
=

23
11

.9(
2.2

)
11

.9(
2.1

)
18

M
5F

21
M

2F
DS

M
-IV

; K
in

de
r-

DI
PS

(K
-D

IP
S)

Cl
in

ica
l:

sth
ym

ic
di

so
rd

er
, m

ajo
r

de
pr

es
sio

n,
an

xi
ety

di
so

rd
er

s
HC

: N
il

IO
W

A
Co

nn
er

s
ra

tin
g s

ca
le

– I
na

tte
nt

io
n-

Ov
er

ac
tiv

ity

sc
ale

. R
ep

or
ted

ra
w

sc
or

St
im

ul
an

ts
we

ce
as

ed
4

8h
ou

rs
pr

io
r t

o t
es

tin
g

Go
/N

o-
Go

; M
ea

su
re

re
sp

on
se

lec
tio

n/
in

hi
bi

tio
n

th
ro

ug
h t

nu
m

be
r o

f f
als

e
ala

rm
s

Ho
bs

on
et

al
.

(2
01

1)
HC

=
34

OD
D/

CD
=

28
AD

HD
±

O
DD

/
CD

=
31

13
.13

(1
.99

)
12

.64
(1

.98
)

13
.32

(1
.81

)

73
.53

%
M

67
.86

%
M

83
.87

%
M

DS
M

-IV
; C

hi
ld

d A
do

les
ce

Ps
yc

hi
atr

ic
As

se
ss

m
en

t
(C

AP
A)

No
t r

ep
or

ted
Co

nn
er

s’
AD

HD
/

DS
M

-IV
P

ar
en

t
an

d T
ea

ch
er

ale
s.

Re
po

rte
d

as
T

sc
or

No
t r

ep
or

ted
fo

r
OD

D/
CD

AD
HD

g
ro

wa
s w

ith
ou

t
m

ed
ica

tio
n f

18
ho

ur
s p

rio
r t

o
tes

tin
g

Go
/N

o-
Go

; M
ea

su
re

m
ot

or
re

sp
on

in
hi

bi
tio

n t
hr

ou
gh

rc
en

tag
e o

f
su

cc
es

sfu
lly

hi
bi

ted
no

-g
o

tri
als

St
op

T
as

k;
M

ot
or

sp
on

se
in

hi
bi

tio
n

m
ea

su
re

d t
hr

ou
gh

RT
e (s

ub
tra

cti
ng

ea
n s

to
p s

ig
na

l
de

lay
fr

om
m

ea
n

re
ac

tio
n t

im
e t

o g
o

tri
als

)

Research on Child and Adolescent Psychopathology

1 3

Ta
bl

e
1

(c
on

tin
ue

Re
fer

en
ce

Su
bj

ec
ts

Ag
e a

M
(S

D)
Se

x
Di

ag
no

sti
c

cr
ite

ria
an

d
as

se
ssm

en
t

Re
po

rte
d

m
or

bi
d

ag
no

se
s

AD
HD

S
ym

pt
om

ea
su

re
M

ed
ica

tio
n

sta
tu

s
Ta

ch
ar

ac
ter

ist
ics

Hu
m

m
er

et
al

.
( 2

01
1)

HC
=

25
DB

Df =
23

DB
D

+
AD

HD
=

15
.1(

1.4
)

14
.80

(1
.3)

14
.7(

1.2
)

13
M

12
F

13
M

10
F

19
M

DS
M

-IV
; S

ch
ed

ul
e

fo
r A

e
cti

Di
so

rd
er

s a
nd

hi
zo

ph
re

ni
a

fo
r S

ch
oo

l-
Ag

ed
C

hi
ld

re
n,

Pr
es

en
t a

Li
fet

im
e V

er
sio

n
(K

-S
AD

AD
HD

+
O

gr
ou

p:
G

an
d s

ep
ar

ati
on

xi
ety

Ad
ol

es
ce

Sy
m

pt
om

ve
nt

or
y –

Co
m

bi
ne

d
AD

HD
sc

or
e.

Re
po

rte
d

as
T

or
e

No
t r

ep
or

ted
St

ro
op

C
ol

ou
r W

or
d

Te
st;

In
hi

bi
tio

n
of

an
au

to
m

ati
c

re
sp

on
se

m
ea

su
re

d
by

nu
m

be
r o

f
co

m
pl

ete
d

ite
m

s
on

S
tro

op
C

ol
ou

r
W

or
d (

SC
W

)
Co

un
tin

g
In

ter
fer

en
ce

st;
In

hi
bi

tio
n

of
an

au
to

m
ati

c
re

sp
on

se
m

ea
su

re
d

by
nu

m
be

r o
f

co
m

pl
ete

d
ite

m
s

Co
nn

er
’s

Co
nt

in
uo

Pe
rfo

rm
an

Ta
sk

(C
CP

T)
; A

ea
ter

nu
m

be
r

of
co

m
m

iss
io

re
e

cts
po

or
er

hi
bi

to
ry

co
nt

ro
l

Be
ha

vi
ou

r R
ati

In
ve

nt
or

y o
f

Ex
ec

ut
iv

e F
un

cti
on

RI
EF

);
In

hi
bi

t
sc

ale
us

ed
to

se
ss

in
hi

bi
to

co
nt

ro
l

Jia
ng

et
al

. (
20

16
)

HC
=

36
OD

D
=

7
OD

D
+

AD
HD

=
17

AD
HD

=
24

12
.92

(1
2.4

5–
13

.40
)

11
.76

(1
0.8

–1
2.6

8)
12

.64
(1

1.6
6–

13
.63

)
12

.17
(1

1.3
5–

13
.00

)
Re

po
rte

d a
s M

an
d

95
%

CI
s

HC 27
M

9F
Al

l O
DD

23
M

AD
HD

22
M

DS
M

-IV
; R

efe
rre

d
fro

m
ou

tp
ati

en
t

cli
ni

c d
ep

ar
tm

en
t

at
a m

en
tal

he
alt

h
ce

nt
re

No
co

m
bo

rb
id

iti
es

Co
nn

er
s P

ar
en

t
Sy

m
pt

om
s

Qu
es

tio
nn

air
e

(H
yp

er
ac

tiv
ity

d
Hy

pe
ra

cti
vi

ty
–

Im
pu

lsi
vi

in
de

xe
s).

po
rte

d
as

ra
w

sc
or

Dr
ug

na
ïv

e
St

ro
op

C
ol

ou
r W

or
d

Te
st;

m
ea

su
re

in
hi

bi
to

co
nt

ro
l/c

ap
ab

ili
ty.

ea
su

re
s a

s t
he

rre
ct

nu
m

be
r i

n
th

e i
nt

er
fer

en
ce

zte

Research on Child and Adolescent Psychopathology

1 3

Ta
bl

e
1

(c
on

tin
ue

Re
fer

en
ce

Su
bj

ec
ts

Ag
e a

M
(S

D)
Se

x
Di

ag
no

sti
c

cr
ite

ria
an

d
as

se
ssm

en
t

Re
po

rte
d

m
or

bi
d

ag
no

se
s

AD
HD

S
ym

pt
om

ea
su

re
M

ed
ica

tio
n

sta
tu

s
Ta

ch
ar

ac
ter

ist
ics

Lu
m

an
et

al
( 2

00
9)

HC
=

50
AD

HD
+

O
DD

=
18

AD
HD

=
20

11
4(

15
)

11
8(

18
)

10
6(

17
)

*r
ep

or
ted

m
on

th
s

56
%

M
69

%
M

ac
ro

cli
ni

ca
l g

ro
up

DS
M

-IV
;

Di
ag

no
sti

c
In

ter
vi

ew
S

ca
le

(D
IS

C-
IV

)

No
t r

ep
or

ted
Ch

ild
B

eh
av

io
ur

ec
kl

ist

an
d T

ea
ch

Ra
tin

g F
or

m
–

In
att

en
tio

n a
nd

pe
ra

cti
vi

ty
/

Im
pu

lsi
vi

sc
ale

s.
Re

po
rte

d
as

ra
w

sc
or

es
DI

SC
-IV

m
pt

om
co

un
t

– I
na

tte
nt

io
n

an
d

Hy
pe

ra
cti

vi
ty

/
Im

pu
lsi

vi
ty

M
eth

yl
ph

en
id

ate

ce
as

ed
at

le
as

t
24

h
pr

io
r t

o
tes

tin
g

St
op

T
as

slo
we

r S
SR

Te
de

m
on

str
ate

s
in

hi
bi

tio
n

pr
ob

lem
s.

RT
eq

ua
l t

o
th

e d
i

er
en

ce
s

be
tw

ee
n m

ea
n

ac
tio

n t
im

e o
n

go
-tr

ial
s a

nd
th

e
m

ea
n s

to
p s

ig
na

l
de

lay

M
ar

tel
et

al
. (

20
13

)
HC

=
24

OD
D

=
18

OD
D

+
AD

HD
=

39
AD

HD
=

3.7
9(

0.9
3)

4.5
6(

1.2
4)

4.4
9(

1.0
7)

4.5
3(

0.9
4)

To
tal

sa
m

pl
e

57
%M

DS
M

-IV
; K

id
di

e
Di

sru
pt

iv
e

Be
ha

vi
ou

r
Di

so
rd

er
s

Sc
he

du
le

(K
-D

BD
S)

No
t r

ep
or

ted
Di

sru
pt

iv
e

Be
ha

vi
ou

r R
ati

Sc
ale

(D
BR

– T
ea

ch
er

an
d

Ca
re

gi
ve

r r
ep

or
ts

In
att

en
tiv

e a
nd

pe
ra

cti
vi

sc
ale

s r
ep

or
ted

ra
w

sc
or

Pa
re

nt
s w

er
e

en
co

ur
ag

to
ce

as
e

ps
yc

ho
sti

m
ul

an
t

m
ed

ica
tio

n
24

–4
8 h

p
rio

r
to

te
sti

ng
if

pr
op

ria
te.

on
e o

n
lo

ng
-a

cti
ng

yc
ho

tro
pi

c
m

ed
ica

tio
n

Sh
ap

e S
ch

oo
l;

ea
su

re
of

sp
on

se
in

hi
bi

tio
n

th
ro

ug
h n

um
be

r
of

co
rre

ct
an

sw
er

s
di

vi
de

d b
y t

im
e t

o
co

m
pl

ete
th

e t
ria

Qi
an

et
al

. (
20

10
)

HC
=

11
6

AD
HD

+
O

DD
=

53
AD

HD
=

9.1
9(

1.6
2)

9.2
5(

1.7
9)

9.0
7(

1.9
2)

97
M

19
F

42
M

11
F

76
M

13
F

DS
M

-IV
; C

lin
ica

l
Di

ag
no

sti
c

In
ter

vi
ew

in
g

Sc
ale

(C
DI

No
co

m
or

bi
di

tie
s

AD
HD

R
ati

Sc
ale

(A
DH

D
RD

-IV
);

AD
HD

tal
sc

or
e

an
d c

om
pu

ted

in
att

en
tio

n a
nd

pe
ra

cti
ve

/
im

pu
lsi

ve
sc

or
es

.
Re

po
rte

d
as

ra
w

sc
or

No
t r

ep
or

ted
Be

ha
vi

ou
r R

ati
ng

ve
nt

or
y o

f
Ex

ec
ut

iv
e F

un
cti

(B
RI

EF
);

In
hi

bi
t

sc
ale

us
ed

as
se

ss
in

hi
bi

to
ry

nt
ro

Research on Child and Adolescent Psychopathology

1 3

Ta
bl

e
1

(c
on

tin
ue

Re
fer

en
ce

Su
bj

ec
ts

Ag
e a

M
(S

D)
Se

x
Di

ag
no

sti
c

cr
ite

ria
an

d
as

se
ssm

en
t

Re
po

rte
d

m
or

bi
d

ag
no

se
s

AD
HD

S
ym

pt
om

ea
su

re
M

ed
ica

tio
n

sta
tu

s
Ta

ch
ar

ac
ter

ist
ics

Ru
bi

a e
t a

l.
( 2

00
8)

g
HC

=
20

CD
=

13
AD

HD
=

14
.0

(1
.9)

12
.9

(2
.2)

13
.2

(1
.4)

Al
l m

ale
DS

M
-IV

;
M

au
ds

ley

Di
ag

no
sti

c
In

ter
vi

No
co

m
or

bi
di

tie
s

St
re

ng
th

s a
nd


cu

lti
es

es
tio

nn
air

e
– H

yp
er

ac
tiv

ity

Sc
or

e.
Re

po
rte

d
as

ra
w

sc
or

Ex
clu

de
d i

f
pr

ev
io

us
ly

po
se

d t
o

sti
m

ul
an

t
m

ed
ica

tio
n

St
op

T
as

k;
M

ot
or

sp
on

se
in

hi
bi

tio
n

m
ea

su
re

d t
hr

ou
gh

RT
e (s

ub
tra

cti
ng

ea
n s

to
p s

ig
na

l
de

lay
fr

om
m

ea
n

re
ac

tio
n t

im
e t

o g
o

tri
als

)
Ru

bi
a e

t a
l.

(2
00

9)
g

HC
=

20
CD

=
13

AD
HD

=
20

14
(2

)
13

(1
)

13
.2

(1
.5)

Al
l m

ale
DS

M
-IV

;
M

au
ds

ley

Di
ag

no
sti

c
In

ter
vi

No
co

m
or

bi
di

tie
s

St
re

ng
th

s a
nd


cu

lti
es

es
tio

nn
air

e
– H

yp
er

ac
tiv

ity

Sc
or

e.
Re

po
rte

d
as

ra
w

sc
or

Ex
clu

de
d i

f
pr

ev
io

us
ly

po
se

d t
o

sti
m

ul
an

t
m

ed
ica

tio
n

Si
m

on
T

as
k;

hi
bi

tio
n o

f a
n

in
co

rre
ct

re
sp

on
se

in
co

ng
ru

en
t

sti
m

ul
i.

M
ea

su
re

d
by

re
sp

on
se

ti
m

to
in

co
ng

ru
en

t
co

m
pa

re
d t

o
co

ng
ru

en
t t

ria
ls

(C
on

i
ct

Re
ac

tio
n

Ti
m

e e
e

ct)
Sa

br
y e

t a
l.

(2
01

1)
h

HC
=

45
AD

in
at

ten
tiv

e =
14

AD
HD

pe
ra

cti
ve

=
15

AD
HD

m
bi

ne
d =

15
CD

=
24

OD
D

=
13

Bi
po

lar
=

9.1
(1

.8)
8.1

7(
1.8

)
8.9

(2
.07

)
8.2

(1
.4)

9.9
(1

.8)
8.1

(1
.9)

11
.1(

0.7
)

25
M

20
F

9 M
5F

12
M

3F
12

M
3F

16
M

8F
6 M

7F
5 M

DS
M

-IV
;

di
ag

no
se

d b
y

tw
o i

nd
ep

en
de

ps
yc

hi
atr

ist
s

us
in

g
a s

em
i-

str
uc

tu
re

d
in

ter
vi

gu
id

ed
by

C
hi

M
en

tal
S

tat
us

am
in

ati
on

No
co

m
or

bi
di

tie
s

No
t r

ep
or

ted
No

t r
ep

or
ted

Qu
an

tit
yi

nh
ib

iti
on

tas

k;
nu

m
be

r o
f

an
sw

er
s c

or
re

ct
Ob

jec
t i

nh
ib

iti
on

tas

k;
nu

m
be

r o
f

an
sw

er
s c

or
re

ct
Nu

m
er

ica
l s

ize

in
hi

bi
tio

n t
as

nu
m

be
r o

f a
ns

we
rs

co
rre

ct
Sc

ha
ch

ar
et

al
.

(2
00

0)
HC

=
33

CD
=

13
AD

HD
=

72
AD

HD
+

C
D

=
47

9.3
(1

.5)
9.5

(1
.4)

9.0
(1

.4)
9.2

(1
.5)

3:
2

6:
1

4:
1

9:
1

*m
:f

ra
tio

DS
M

-IV
; P

ar
en

t
In

ter
vi

ew
fo

r
Ch

ild
S

ym
pt

om
s

(P
IC

S)
an

d
Te

ac
he

r
Te

lep
ho

In
ter

vi
ew

(T
TI

)

To
tal

sa
m

pl
e:

Ex
clu

de
d i

f
ps

yc
ho

sis
,

or
cl

in
ica

lly

sig
ni

c
an

t
m

oo
d o

r a
nx

iet
y

di
so

rd
er

p
re

se
nt

HC
: F

re
e o

f
co

m
or

bi
d

di
ag

no
se

Pa
re

nt
In

ter
vi

fo
r C

hi
ld

m
pt

om
s

an
d T

ea
ch

Te
lep

ho
ne

ter
vi

ew
.

Re
po

rte
d

as
ra

w
sc

or
es

St
im

ul
an

t
m

ed
ica

tio
n

ce
as

ed
4

8h
rs

pr
io

r t
o t

es
tin

St
op

-si
gn

al;
D

e
cit

s
of

in
hi

bi
to

co
nt

ro
l m

ea
su

re
d

th
ro

ug
h

SS
RT

Research on Child and Adolescent Psychopathology

1 3

Ta
bl

e
1

(c
on

tin
ue

Re
fer

en
ce

Su
bj

ec
ts

Ag
e a

M
(S

D)
Se

x
Di

ag
no

sti
c

cr
ite

ria
an

d
as

se
ssm

en
t

Re
po

rte
d

m
or

bi
d

ag
no

se
s

AD
HD

S
ym

pt
om

ea
su

re
M

ed
ica

tio
n

sta
tu

s
Ta

ch
ar

ac
ter

ist
ics

Sc
ho

em
ak

er
et

al
.

( 2
01

2)
HC

=
56

DB
Df =

33
AD

HD
=

61
AD

HD
+

D
BD

=
52

55
.66

(7
.18

)
51

.88
(8

.29
)

55
.20

(7
.41

)
54

.12
(6

.80
)

*r
ep

or
ted

m
on

th
s

69
.6%

M
81

.8%
M

80
.3%

M
82

.7%
M

DS
M

-IV
-T

Co
ns

en
su

s
be

tw
ee

n c
hi

ps
yc

hi
atr

ist
an

d
cli

ni
ca

l c
hi

ps
yc

ho
lo

gi
st

us
in

g
sy

m
pt

m
ea

su
re

s
an

d c
lin

ica
l

in
ter

vi
ew

in
g

No
ne

re
po

rte
d

Ch
ild

B
eh

av
io

Ch
ec

kl
ist

–
Pa

re
nt

an
d

Te
ac

he
r R

ep
or

t:
At

ten
tio

n
Pr

ob
lem

s S
ca

le.

Re
po

rte
d

as
T

or
es

No
ch

ild
re

n
we

on
m

ed
ica

tio
n

Go
/N

o-
Go

; M
ea

su
re

in
hi

bi
to

sk
ill

s t
hr

ou
gh

th
e

nu
m

be
r o

f N
o-

tri
als

co
rre

ctl
y n

ot
–

pr
es

se
d d

iv
id

th
e t

ot
al

nu
m

be
r o

f
No

-G
o

tri
als

M
od

i
ed

S
na

De
lay

; M
ea

su
re

in
hi

bi
to

sk
ill

s t
hr

ou
gh

e n
um

be
r o

f
in

ter
va

ls
th

at
th

e
ch

ild
co

m
pl

ied

wi
th

al
l t

as
k r

ul
es

Sh
ap

e S
ch

oo
l –

hi
bi

t C
on

di
tio

M
ea

su
re

in
hi

bi
to

ry
sk

ill
s

th
ro

ug
h n

um
be

r o
f

co
rre

ct
re

sp
on

se
s

di
vi

de
d b

y t
he

to
tal

m
be

r o
f t

ria
ls

Sh
ua

i e
t a

l.
(2

01
1)

HC
=

76
AD

HD
+

O
DD

=
38

AD
HD

=
76

AD
HD

+
Le

ar
ni

Di
so

rd
er

=
38

10
.21

(2
.30

)
10

.34
(2

.53
)

10
.24

(2
.40

)
10

.38
(2

.56
)

Al
l m

ale
DS

M
-IV

;
St

ru
ctu

re
d

in
ter

vi
ew

nd
uc

ted
by

yc
hi

atr
ist

Ti
c a

nd
m

oo
d

di
so

rd
er

s
No

t r
ep

or
ted

No
m

ed
ica

tio
n

pr
io

r t
o t

es
tin

g
St

ro
op

C
ol

ou
r a

W
or

d T
es

t;
to

ea
su

re
in

hi
bi

tio
n

by
an

in
ter

fer
en

sc
or

Research on Child and Adolescent Psychopathology

1 3

Ta
bl

e
1

(c
on

tin
ue

Re
fer

en
ce

Su
bj

ec
ts

Ag
e a

M
(S

D)
Se

x
Di

ag
no

sti
c

cr
ite

ria
an

d
as

se
ssm

en
t

Re
po

rte
d

m
or

bi
d

ag
no

se
s

AD
HD

S
ym

pt
om

ea
su

re
M

ed
ica

tio
n

sta
tu

s
Ta

ch
ar

ac
ter

ist
ics

Sk
og

an
et

al
( 2

01
4)

HC
=

45
5

OD
D

=
20

5
AD

HD
+

O
DD

=
23

5
AD

HD
=

15
0

41
.7(

1.3
)

41
.8(

1.4
)

41
.7(

1.3
)

41
.5(

1.2
)

*r
ep

or
ted

m
on

th
s

23
9 M

21
6F

10
6 M

99
F

13
6 M

99
F

80
M

70
F

DS
M

-IV
-T

Pr
es

ch
oo

l A
ge

yc
hi

atr
ic

As
se

ss
m

en
t

in
ter

vi
ew

(P
AP

No
t r

ep
or

ted
PA

PA
sy

m
pt

ra
tin

gs
. R

ep
or

ted

as
a

sy
m

pt
om

un
t

Ni
l ps

yc
ho

sti
m

ul
an

t
m

ed
ica

tio
n

at
th

e t
im

e o
f

as
se

ss
m

en
t

NE
PS

Y
su

bt
es

t –

St
atu

e;
m

ea
su

of
in

hi
bi

tio
n.

Sc
or

in
g:

tw
o

po
in

ts
pe

r 5
-s

in
ter

va
l a

po
in

ted
de

du
cte

d
fo

r m
ov

em
en

ts
pe

r
in

ter
va

l
Sp

in
th

e P
ot

s;
as

se
ss

es
ab

ili
ty

su
pp

re
ss

pr
ep

ot
en

t
re

sp
on

se
. R

ep
or

ted

as
an

im
pu

lsi
vi

sc
or

e
Sk

og
an

et
al

.
( 2

01
5)

HC
=

11
7

OD
D

=
39

AD
HD

=
10

4
An

xi
ety

=
48

To
tal

sa
m

pl
e

41
.8(

1.3
)

*r
ep

or
ted

m
on

th
s

65
M

52
F

21
M

18
F

66
M

38
F

27
M

21
F

DS
M

-IV
;

Pr
es

ch
oo

l A
ge

yc
hi

atr
ic

As
se

ss
m

en
t

in
ter

vi
ew

(P
AP

No
t r

ep
or

ted
PA

PA
sy

m
pt

ra
tin

gs
. R

ep
or

ted

as
a

sy
m

pt
om

un
t.

Re
po

rte
d

as
a

m
ea

n f
or

th
e

sa
m

pl
e o

ve
ra

Ni
l ps

yc
ho

sti
m

ul
an

t
m

ed
ica

tio
n

at
th

e t
im

e o
f

as
se

ss
m

en
t

Be
ha

vi
ou

r R
ati

In
ve

nt
or

y o
f

Ex
ec

ut
iv

e
Fu

nc
tio

n –

Pr
es

ch
oo

l v
er

sio
n

(B
RI

EF
-P

);
In

hi
bi

t
sc

ale
us

ed
to

se
ss

in
hi

bi
to

co
nt

ro
l

Va
n G

oo
ze

n e
t a

l.
(2

00
4)

HC
=

36
OD

D
=

15
OD

D
+

AD
HD

=
26

9.2
(1

.2)
10

.1(
1.2

)
9.5

(1
.6)

14
M

22
F

36
M

5F
(T

ot
al

OD
D)

DS
M

-IV
; D

IS
C-

P
OD

D
gr

ou
p:

pr
es

sio
n,

an
xi

ety
, O

CD
,

To
ur

ett
es

/
tic

di
so

rd
er

,
en

ur
es

is/
en

co
pr

es
is

OD
D

+
AD

gr
ou

p:
C

de
pr

es
sio

n,
an

xi
ety

, O
CD

,
To

ur
ett

es
/ti

c
di

so
rd

er
, e

nu
re

sis

an
d e

nc
op

re
sis

HC
: A

DH
D

(n
=

Ch
ild

B
eh

av
io

Ch
ec

kl
ist

–
Pa

re
nt

an
d

Te
ac

he
r R

ep
or

t:
At

ten
tio

n
Pr

ob
lem

s S
ca

le.

Re
po

rte
d

as
T

or
es

No
t r

ep
or

ted
St

ro
op

co
lo

ur
w

or
d

tes
t;

Re
qu

ire
s

in
hi

bi
tio

n o
f

an
ov

er
lea

rn
ed

to
m

ati
c

re
sp

on
se

.
M

ea
su

re
d v

ia
SC

W
T-

in
tti

m
e;

th
e t

im
e r

eq
ui

re
d

to
co

m
pl

ete
th

e
sti

m
ul

us
se

t i
n

th
e i

nt
er

fer
en

co
nd

iti
on

Research on Child and Adolescent Psychopathology

1 3

Ta
bl

e
1

(c
on

tin
ue

Re
fer

en
ce

Su
bj

ec
ts

Ag
e a

M
(S

D)
Se

x
Di

ag
no

sti
c

cr
ite

ria
an

d
as

se
ssm

en
t

Re
po

rte
d

m
or

bi
d

ag
no

se
s

AD
HD

S
ym

pt
om

ea
su

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Research on Child and Adolescent Psychopathology

1 3

apparent symmetry. This was also reected by the Trim and
Fill test which reected minimal change in the estimated
eect between groups (Duval and Tweedie 2000). Studies
included in the current review underwent a quality appraisal
by two independent reviewers utilising the Joanna Briggs
Institute Critical Appraisal Tools (Table 2; Moola et al.
2017). All studies in the review were deemed to be of
adequate quality and as such, were included for final
analysis.

Calculation of Effect Sizes

If papers reported on the same sample, the first paper
published was utilised as the primary study. Provided they
were dierent from the primary paper, additional measures
from subsequent papers were included. Data from these
papers were treated as one sample in the analyses. Two
papers (Bahcivan Saydam et al. 2015; Sabry et al. 2011)
reported data by subgroups. For the purpose of this meta-
analysis, a single eect size was calculated according to the
formula recommended by Borenstein (2009).

All extracted data were entered into and analysed by
Comprehensive Meta-Analysis (CMA) software (Version
3). Meta-analyses were conducted for four outcome
types: performance measures for cool inhibitory control,
performance measure for hot inhibitory control, rating
scales, and ADHD symptoms. Comparisons were made
between HC, ODD/CD, ODD/CD + ADHD, and ADHD
groups. Results are presented as a mean eect size, reected
as Hedges g with associated 95% condence intervals acting
as a common metric between studies. A random eects
model was utilised to account for greater between study
variance.

Between Study Variability and Outliers

Between-study variability was examined through a Q test
of heterogeneity. A signicant Q suggests that reasons for
variance between studies should be considered. For example,
outliers and potential moderators should be examined
through meta-regression or other appropriate methods
(Borenstein 2009). Sensitivity analyses were conducted
for each analysis where significant heterogeneity was
indicated; outliers are only reported if they were removed
from analyses.

Potential Moderators

Each study implemented a performance measure of
inhibitory control, however tasks usually varied between
studies. While all tasks are designed to assess the same
underlying construct of inhibitory control, we know that
there can be variability between tasks due to task impurity

(i.e., due to the nature of cognitive ability, performance
is aected by other cognitive processes depending on the
nature of the task; Anderson 2002). As such, exploratory
subgroup analyses were conducted as a function of task
paradigm (e.g., Stroop, Go/No-Go, Continuous Performance
Tasks) to explore dierence of eect size between tasks.
Additionally, rating scales were reported as mean item
score, subscale total, or T-score and ADHD symptoms
were reported as T-scores, raw scores, or symptom counts.
These variances between studies were also considered as a
potential moderator.

Age was considered as a moderator as ages of participants
ranged from approximately three years to 15 years. Age
was coded as a continuous variable, with weighted mean
age calculated for each study if not provided. Gender was
coded as a continuous variable, representing the percentage
of males in the sample and was calculated when required.
Medication was considered as a moderator, however most
studies ceased children’s medication prior to testing and as
such, was not included in moderation analyses.

Performance Measures for Cool Inhibitory Control

Following are the analyses for performance on measures
of cool inhibitory control. Eect size and heterogeneity
analyses are found in Table 3. Forest plots can be found in
the Supplementary materials.

ODD/CD vs. HC. Across 15 studies, children with
ODD/CD were found to have signicantly more diculties
with cool inhibitory control compared to healthy controls
(g = -0.58, p < 0.001); with signicant heterogeneity. Age was not found to be a signicant moderator (p = 0.42), nor was percentage of males in the sample (p = 0.89). However, subgroup analyses revealed signicant overall eects for the following tasks: Shape Shift (k = 2, g = -0.74, p < 0.001), Go/No-Go (k = 2, g = -0.75, p < 0.001), Stop Task (k = 4, g = 0.36, p = 0.17), and tasks employing the Stroop paradigm (k = 5, g = -0.83, p = 0.046)., ODD/CD vs. ADHD. A meta-analysis of 13 studies did not reveal a signicant dierence between children with ODD/CD and ADHD. Moderation analyses revealed that age did not signicantly contribute to between study variance (p = 0.68) and nor did percentage of males in the sample (p = 0.51). Further exploratory subgroup analyses identied the only task with a signicant dierence in performance was the Statue Task (g = 0.38, p < 0.001), however only one study utilised this assessment. ODD/CD vs. ODD/CD + ADHD. Across 13 studies, children with a comorbid diagnosis of ODD/CD + ADHD were found to perform more poorly on tasks of inhibitory control compared to those of a single ODD/CD diagnosis (g = 0.18, p = 0.03). The data were found to be homogenous and no follow-up moderation was required. Exploratory Research on Child and Adolescent Psychopathology 1 3 Ta bl e 2 Q ua lit y a pp ra isa l o f i nc lu de d s tu di es St ud y 1. W er e t he cr ite ria fo r i nc lu sio n i n th e s am pl e c lea rly de n ed ? 2. W er e t he st ud y su bj ec ts an d t he se tti ng de sc rib ed in de tai l? 3. W as th e e xp os ur e (in hi bi to ry co nt ro l) m ea su re d i n a va lid an d r eli ab le wa y? 4. W er e o bj ec tiv e, sta nd ar d cr ite ria us ed fo r m ea su re m en t o f t he co nd iti on (O DD / CD )? 5. W er e co nf ou nd in g fac - to rs id en ti ed ? 6. W er e s tra teg ies to de al wi th co nf ou nd in g fac to rs sta ted ? 7. W er e t he ou tco m es m ea su re d i n a va lid an d r eli ab le wa y? 8. W as ap pr op ria te sta tis tic al an aly sis us ed ? Al br ec ht et al . ( 2 00 5) + + + ? + + + + An to ni ni et al . (2 01 5) + + + + + + + + Ba hc iv an et al . (2 01 5) + + + ? - - + + Ba na sc he ws ki et al . (2 00 4) + + + ? + + + + Bo rk ow sk a e t a l. (2 01 6) + + + ? - - + + Ez pe let a a nd Gr an er o ( 20 15 ) + + + + + + + + Gl en n e t a l. (2 01 7) + + + + + + + + Gu nt he r e t a l. (2 00 6) + + + + + + + + Ho bs on et al . (2 01 1) + + ? + + + + + Hu m m er et al . (2 01 1) + + + + + + + + Jia ng et al . ( 20 16 ) + + + ? - - + + Lu m an et al . ( 20 09 ) + + + + + + + + M ar tel et al . ( 20 13 ) + + + + + + + + Qi an et al . ( 20 10 ) + + + + + + + + Ru bi a e t a l. (2 00 9) + + + + + + + + Ru bi a e t a l. (2 00 8) + + + + + + + + Sa br y e t a l. (2 01 1) + + ? ? - - ? + Sc ha ch ar et al . (2 00 0) + + + ? + + + + Sc ho em ak er et al . (2 01 2) + + + + + + + + Sh ua i e t a l. (2 01 0) + + + ? + + + + Sk og an et al . (2 01 4) + + + + + + + + Research on Child and Adolescent Psychopathology 1 3 subgroup analyses revealed the following tasks had significant differences between groups: Continuous Performance Task (CPT; k = 3, g = 0.56, p = 0.003), and Statue task (k = 1, g = 0.51, p < 0.001). ODD/CD + ADHD vs. ADHD. Pooling all measures of 16 studies did not reveal a signicant dierence between children with ODD/CD + ADHD and ADHD only (p = 0.88). Moderation analyses revealed that age did not signicantly account for between study variance (p = 0.65) and nor did gender (p = 0.66). Exploratory subgroup analysis revealed that the CPT was the only task where a signicant dierence in performance was observed (k = 2, g = -0.81, p = 0.002); with poorer performance in children with ODD/ CD + ADHD. ODD/CD + ADHD vs. HC. An analysis of 19 studies revealed that children with a comorbid diagnosis of ODD/ CD and ADHD performed more poorly than healthy controls (g = -0.47, p < 0.001). Children with ODD/ CD + ADHD were found to perform more poorly on most tasks, including: CPT (k = 3, g = -0.49, p = 0.007), Spin the Pots (k = 1, g = -0.29, p < 0.001), Statue (k = 1, g = -0.34, p < 0.001), Stop Task (k = 4, g = -0.34, p 0.01), Go/No-Go (k = 4, g = -0.75, p < 0.001), and the Stroop paradigm (k = 7, g = -0.59, p < 0.001), Performance Measures for Hot Inhibitory Control Meta-analysis could not be conducted due to one study using a measure assessing hot inhibitory control. Schoemaker et al. (2012) administered the Modied Snack Delay as a measure of inhibitory control in a motivationally salient context. All three clinical groups (i.e., Disruptive Behaviour Disorders (DBD), ADHD, and ADHD + DBD) were found to have poorer inhibitory control than healthy controls (p < 0.01). Signicant main eects of ADHD and DBD were found, as well as a signicant interaction eect of ADHD and DBD. Rating Scales The analyses for group dierences on ratings of inhibitory control are presented below. Eect size and heterogeneity analyses are found in Table 4. All studies included in the following analyses reported using the BRIEF as a measure of inhibitory control. Due to so few studies included for each analysis, follow-up moderation analyses were not conducted where heterogeneity was signicant. Variability is likely due to the limited number of studies included, as well as dierent metrics utilised between studies (i.e., T-scores, raw scores, item scores). Forest plots can be found in the Supplementary materials. ODD/CD vs. HC. Three studies revealed no signicant dierences between children with ODD/CD and healthy controls (p = 0.103). Although each study utilised the * I tem 4 re qu ire d u se of a cli ni ca l i nt er vi ew to gu id e d iag no sis ; s tu di es re ce iv ed an “u ns ur e” if o nl y d iag no sti c c rit er ia we re pr ov id ed (e .g. , D SM -IV ). * I tem 8 wa s a ss es se d a s t o w he th er th e a na lys is wa s a pp ro pr iat e f or th e p ap er ’s ow n re se ar ch qu es tio n. Se e M oo la et al. (2 01 7) fo r f ul l d eta ils o f t he cr ite ria fo r e ac h i tem . Ta bl e 2 (c on tin ue d) St ud y 1. W er e t he cr ite ria fo r i nc lu sio n i n th e s am pl e c lea rly de n ed ? 2. W er e t he st ud y su bj ec ts an d t he se tti ng de sc rib ed in de tai l? 3. W as th e e xp os ur e (in hi bi to ry co nt ro l) m ea su re d i n a va lid an d r eli ab le wa y? 4. W er e o bj ec tiv e, sta nd ar d cr ite ria us ed fo r m ea su re m en t o f t he co nd iti on (O DD / CD )? 5. W er e co nf ou nd in g fac - to rs id en ti ed ? 6. W er e s tra teg ies to de al wi th co nf ou nd in g fac to rs sta ted ? 7. W er e t he ou tco m es m ea su re d i n a va lid an d r eli ab le wa y? 8. W as ap pr op ria te sta tis tic al an aly sis us ed ? Sk og an et al . ( 2 01 5) + + + + + + + + Va n G oo ze n e t a l. (2 00 4) + + + + + + + + W ier se m a e t a l. (2 00 6) + + + ? + + + + Xu et al . ( 20 17 ) + + + ? + + + + Research on Child and Adolescent Psychopathology 1 3 same measure, a non-signicant dierence may be due to dierences in reporting between each study; with Ezpeleta (2015) reporting an average total raw score, Hummer (2011) reported an average T-score, and Skogan (2015) an average item score. ODD/CD vs. ADHD. Two studies revealed that children with ADHD had more diculties with inhibitory control than children with ODD/CD (g = 0.97, p = 0.001). While het- erogeneity was not signicant, the 95% condence intervals suggest greater variability in the presence and severity of parent-reported inhibitory control decits. As such, further empirical studies are needed. ODD/CD vs. ODD/CD + ADHD. The meta-analysis of two studies showed that children with a comorbid diagnosis of ODD/CD + ADHD were more likely to be rated as hav- ing more diculties with inhibitory control compared to children with ODD/CD only. However, due to few studies, conclusions are tentative. ODD/CD + ADHD vs. ADHD. Similarly, only two stud- ies used the BRIEF to assess dierences between children with ODD/CD + ADHD and ADHD (Ezpeleta, 2015; Qian, 2010). Whilst both studies employed the same metric (raw scores on the Inhibit scale), no signicant dierences were observed (p = 0.87). ODD/CD + ADHD vs. HC. Three studies revealed that children with ODD/CD + ADHD were rated as having more diculties with inhibitory control compared to healthy con- trols (g = -1.95, p = 0.001). Signicant heterogeneity was observed; however, this is likely due to dierences in metrics employed and few studies in the analysis. ADHD Symptomatology The analyses for group dierences on measures of ADHD symptomatology are below. Eect size and heterogeneity statistics are reported in Table 5. Forest plots can be found in the Supplementary materials. ODD/CD vs. HC. Across 12 studies, children with ODD/CD were found to have signicantly more symptoms of ADHD compared to healthy controls (g = -1.59, p < 0.001). However, studies were found to be signicantly heterogenous. Between measures, three types of metrics Table 3 Eect size and heterogeneity analysis for cool inhibitory control performance measures k = number of samples included N = number of participants g = Hedge’s g CI = condence interval I2 = proportion of variability between studies. Eect Size Analysis Heterogeneity Analysis Comparison k N g SE 95%CI Z-value p Q df (Q) I2 p ODD/CD vs HC 15 1931 -0.58 0.16 -0.90 – -0.26 -3.54 < 0.001 93.76 14 85.07 < 0.001 ODD/CD vs ADHD 13 993 0.06 0.07 -0.09 – 0.20 0.80 0.43 13.34 12 10.05 0.035 ODD/CD vs ODD/CD + ADHD 13 1010 0.18 0.07 0.02 – 0.35 2.13 0.03 15.96 12 24.81 0.19 ODD/CD + ADHD vs ADHD 16 1458 -0.01 0.08 -0.17 – 0.15 -0.15 0.88 27.86 15 46.17 0.02 ODD/CD + ADHD vs HC 19 2414 -0.47 0.005 -0.60 – -0.34 -7.01 < 0.001 27.61 18 34.81 0.07 Table 4 Eect size and heterogeneity analysis for inhibitory control on rating scales of inhibitory control k = number of samples included N = number of participants g = Hedge’s g CI = condence interval I2 = proportion of variability between studies. Eect Size Analysis Heterogeneity Analysis Comparison k N g SE 95%CI Z-value p Q df (Q) I2 p ODD/CD vs HC 3 793 -0.81 0.50 -1.78 – 0.16 -1.63 0.10 34.44 2 94.19 < 0.001 ODD/CD vs ADHD 2 217 0.97 0.29 0.40 – 1.55 3.32 0.001 3.15 1 68.27 0.07 ODD/CD vs ODD/CD + ADHD 2 109 1.12 0.44 0.26 – 1.98 2.55 0.01 3.42 1 70.79 0.06 ODD/CD + ADHD vs ADHD 2 175 -0.10 0.61 -1.29 – 1.08 -0.17 0.87 8.54 1 88.29 0.003 ODD/CD + ADHD vs HC 3 767 -1.95 0.57 -3.07 – -0.83 -3.41 0.001 21.97 2 90.90 < 0.001 Research on Child and Adolescent Psychopathology 1 3 were reported: T-scores, subscale raw scores, and symptom counts. Subgroup analyses were conducted across these metrics, with T-scores (k = 6, g = -2.02, p < 0.001), symptom counts (k = 3, g = -0.65, p = 0.02), and subscale raw scores (k = 4, g = -1.43, p = 0.003) showing signicant dierences between groups. Gender was found to signicantly moderate eect size, with a greater percentage of boys in the sample with effect size (ß = -0.05, p < 0.001). Age was not a signicant moderator (p = 0.20). ODD/CD vs. ADHD. A meta-analysis of 11 studies did not reveal a signicant dierence in ADHD symptomatology was observed between children with ODD/CD and ADHD with all measures pooled (p = 0.29). Age was found to signicantly contribute to between study variance (ß = -0.15, p = 0.016), as was gender with eect size associated with a greater percentage of boys in the sample (ß = -0.04, p < 0.001). Exploratory subgroup analyses were conducted to examine dierences between metrics reported, however no signicant dierences were noted (all p > 0.052).

ODD/CD vs. ODD/CD + ADHD. Children with a
comorbid diagnosis of ODD/CD + ADHD were reported
to have signicantly more ADHD symptoms than children
with ODD/CD alone across 13 studies (g = 0.74, p = 0.004).
Studies were also found to be heterogenous; as such,
moderation analyses were conducted. Age was not found
to signicantly explain between study variance (p = 0.86),
however proportion of males in the sample was signicantly
associated with eect size (ß = -0.04, p < 0.001). However, this may be due to the majority of studies that reported on gender had more than 75% of males in the sample. Subgroup analyses revealed measures reported as symptom counts (k = 2, g = 1.46, p = 0.02), subscale raw scores (k = 5, g = 0.49, p = 0.005), and T scores (k = 6, g = 0.72, p = 0.03) indicated children with ODD/CD + ADHD showed greater ADHD symptomatology. ODD/CD + ADHD vs. ADHD. Across 16 studies, children with ODD/CD + ADHD were reported to have more ADHD symptomatology than children with ADHD alone (g = -0.49, p < 0.001). Studies showed significant heterogeneity. Age did not moderate between study variance (p = 0.72), nor did gender (p = 0.06). Through subgroup analyses, only subscale raw scores (k = 8, g = -0.71, p = 0.001) and symptom counts (k = 3, g = -0.35, p = 0.008) revealed a signicant dierence between groups. ODD/CD + ADHD vs. HC. Meta-analysis of 19 studies revealed children with a comorbid diagnosis of ODD/ CD + ADHD had signicantly more symptoms of ADHD compared to healthy controls (g = -3.23, p < 0.001). Age was not a signicant moderator of eect size (p = 0.40) and nor was gender (p = 0.47). Subgroup analyses revealed all metrics of assessing ADHD symptomatology indicated significant differences between groups; T-scores (k = 6, g = -3.16, p < 0.001), subscale raw scores (k = 6, g = -2.77, p < 0.001), and symptom counts (k = 4, g = -4.49, p < 0.001). Discussion This meta-analysis explored whether children with a diagnosis of ODD and/or CD had more decits of inhibitory control compared to healthy controls, independent of a diagnosis of ADHD. To the best of our knowledge, this meta-analysis is the rst to assess inhibitory control performance in a clinical sample of children with ODD/CD, using a range of inhibitory control measurement approaches in both hot and cool contexts, and also giving consideration to ADHD symptomatology. ADHD, ODD, and CD: Categorical Disorders or Dimensions of the Same Pathology? Overall, results across multiple meta-analyses demonstrated that children with ODD/CD and ADHD have similar ADHD symptomatology and performance on tasks of inhibitory Table 5 Eect size and heterogeneity analysis for ADHD symptomatology between groups k = number of samples included N = number of participants g = Hedge’s g CI = condence interval I2 = proportion of variability between studies. Eect Size Analysis Heterogeneity Analysis Comparison k N g SE 95%CI Z-value p Q df (Q) I2 p ODD/CD vs HC 12 1715 -1.59 0.27 -2.11—-1.07 -6.02 < 0.001 136.77 11 91.92 < 0.001 ODD/CD vs ADHD 11 872 0.35 0.33 -0.30 – 1.00 1.05 0.29 159.88 10 93.75 < 0.001 ODD/CD vs ODD/CD + ADHD 13 1010 0.74 0.26 0.24 – 1.23 2.88 0.004 128.31 12 90.65 < 0.001 ODD/CD + ADHD vs ADHD 14 1322 -0.49 0.13 -0.73 – -0.25 -3.94 < 0.001 51.46 13 74.74 < 0.001 ODD/CD + ADHD vs HC 15 2148 -3.23 0.28 -3.79—-2.68 -11.44 < 0.001 164.63 14 91.50 < 0.001 Research on Child and Adolescent Psychopathology 1 3 control. These findings further contribute to current discussions as to whether ADHD and ODD/CD are best captured under a dimensional approach to psychopathology (Frick and Nigg 2012; Wakschlag et al. 2018). Specically, results found that: (1) when children with ODD were compared to those with ADHD, there was no signicant dierence in cool inhibitory control performance; (2) this held true for the majority of tasks when subgroup analyses were conducted; (3) there was no signicant dierence between ODD/CD children and ADHD children on measures of parent reported ADHD symptomatology; (4) despite an absence of a clinical ADHD diagnosis, children with ODD still had similar ADHD symptomatology to those children with a clinical diagnosis of ADHD; (5) children with a combined diagnosis of ODD/CD + ADHD were found to have worse inhibitory control than children with ODD/ CD alone, but not signicantly dierent from children with ADHD alone; (6) children with a comorbid diagnosis were found to have signicantly greater ADHD symptomatology compared to both ODD/CD and ADHD respectively. In sum, it appears that children with ODD/CD and ADHD have diculties with inhibitory control and similar ADHD symptomatology. Future diagnostic manuals may need to consider these diagnoses within a dimensional framework of psychopathology; rather than a categorical framework. Interestingly, previous authors have supported similar conclusions. For example, Blair et. al (2018) suggested that while children with conduct problems have inhibitory control dysfunction, this is likely manifested as ADHD symptoms. The previous meta-analysis on the Stop task by Oosterlaan et al. (1998) revealed similar results. Compared to healthy controls, children with CD were found to have more difficulties with inhibitory control as indicated by the stop signal reaction time (SSRT), however, no signicant dierences were found between ADHD and CD, or ADHD + CD and ADHD. It has also been previously established that ADHD, ODD, and CD are often co-morbid (Angold et al. 1999) and share common risk factors for development such as decits in inhibitory control (Matthys and Lochman 2017). Signicant comorbidity is challenging for categorical approaches to diagnosis. When signicant comorbidity exists, a dimensional approach can be more clinically meaningful. Even for children with non-clinical levels of disruptive behaviour, diculties with inhibitory control have been demonstrated (Schoemaker et al. 2013; Woltering et al. 2016). This would suggest that diculties with inhibitory control have been present across varying severities of disruptive behaviour. Researchers have discussed the lack of utility in classifying ADHD, ODD, and CD as separate disorders, highlighting that there is evidence to support the three disorders sharing common aetiology across a number of factors (not simply neurobiological deficits), and as such, should be considered as related disorders (e.g., Frick and Nigg 2012; Wakschlag et al. 2018; Matthys and Lochman 2017). Do Children with ODD/CD have Hot or Cool Inhibitory Control Decits? Unfortunately, only one study (Schoemaker et al. 2012) utilised a measure of hot inhibitory control, and as such, further meta-analyses could not be conducted. Previous authors have suggested that disruptive behaviour (in particular CD), is associated with hot inhibitory control (Rubia 2011; Zhu et al. 2018). This makes theoretical sense as children with ODD/CD often engage in more risk-taking and rule-breaking behaviours than their healthy peers and are less sensitive to reward processing (Matthys and Lochman 2017). Tasks that involve hot executive functions usually assess this behaviour with tasks that are motivationally salient (Antonini et al. 2015). However, the dearth of research utilising or reporting on performance tasks that involve hot inhibitory control mean that no empirical conclusions can be drawn yet. A small signicant eect was found when comparing measures of cool inhibitory control, suggesting that children with ODD and/or CD may have more difficulties with inhibitory control compared to healthy peers. Additionally, despite varying tasks assessing inhibitory control, these diculties persist for children with a clinical diagnosis of ODD/CD. This result supports those found in the review by Oosterlaan et al. (1998), which assessed inhibitory control decits in CD samples, independent of ADHD; nding that children with CD performed signicantly worse than healthy controls. However, Oosterlaan et al. (1998) reviewed performance on the Stop Signal Task only. The Impact of How Inhibitory Control is Measured The current review included papers that specifically identified inhibitory control as a variable of interest. However, measures such as the Continuous Performance Task (CPT) have previously defined the same outcome measure (number of commissions) as either an index of attention or inhibitory control. One could argue that inhibitory control subserves attention; however, the conceptualisation of executive function and where each cognitive process sits within the framework is an ongoing theoretical discussion in the executive function literature (Baggetta and Alexander 2016; Packwood et al. 2011). Further, the Stop Signal and Stroop tasks were most often used to assess cool inhibitory control across all comparisons between groups. Fewer studies utilised the Go/No-Go task, Continuous Performance Tasks, and the Statue and Spin the Pots tasks were used the least. It is challenging to make strong conclusions about dierences in performance Research on Child and Adolescent Psychopathology 1 3 on these tasks when so few studies are utilising the same measure. Additionally, it limits our understanding as to which tasks contribute to an overall eect of inhibitory control deficits between groups. Future research would benet from including several measures of inhibitory control to determine dierences between tasks. Unfortunately, these diculties will always be inherent in the eld of cognitive research, due to task impurity. Strong conclusions cannot be drawn based on ratings with ecological measures (i.e., BRIEF) due to the paucity of studies. A maximum of three studies were included for most comparisons, with each generally using a dierent metric of the BRIEF. It is likely that the use of dierent metrics (T-scores, average raw subscale scores, and average item scores) contributed to greater variability between studies. This highlights two important points. First, that there is a need for further research using ecologically valid measures of inhibitory control; as these measures translate to behaviours observed in real world settings (Toplak et al. 2013). Second, that consistent use of metric is important to eectively compare and replicate research. Where available, T-scores are most meaningful as results can be compared across measures. Overall, there is much work yet to be done in the use of rating scales of executive function. The Impact of Inhibitory Control Decits on Clinical Interventions for ODD/CD Children Explaining disruptive behaviours from a perspective of skill decit, interacting with environmental factors (e.g., parenting style, peer inuences) highlights the importance of utilising alternative approaches to intervention. For example, children who display disruptive behaviours are more likely to elicit unhelpful parenting responses (Burke et al. 2008). Harsher parenting styles are known to be associated with increases in disruptive behaviour; and so, the cycle is perpetuated (Combs-Ronto et al. 2009). This may be an important consideration as to why learning- based interventions are not successful for some children with ODD/CD. As such, alternative interventions such as the Collaborative Problem Solving approach (Greene 2014; Pollastri et al. 2019) may be useful as such approaches consider neurodevelopmental dierences that may impact upon behaviour. We can utilise such approaches to assist children to cope with or compensate for the skill decit; which may improve clinical outcomes beyond what they would have been with only a learning-based intervention. Limitations and Future Directions. Limitations within the studies impact the quality of the meta- analysis overall; including, small sample sizes, dierences in methodology and design, and demographic (Borenstein 2009). While subgroup analyses allowed for further examination between tasks, most tasks had fewer than ve studies included. Underpowered analyses contribute to greater variance between studies which cannot be adequately explored through moderation due to so few studies. Further, the meta-analyses are limited by the nature of the data they assess; mean effect sizes. A more comprehensive examination of inhibitory control between clinical groups, moderated by ADHD symptoms, could be conducted via an analysis of all individual data from each of the included studies (Stewart and Clarke 1995). The use of individual data collected from each study would allow for more comprehensive and sophisticated analyses to investigate the range of ODD/CD and ADHD symptomatology. This may facilitate further understanding of a dimensional approach to psychopathology for these disorders. Additionally, if all individual data is collected, this may allow for T-scores to be used in more analyses, providing a more standardised approach. Future research into the aetiology of disruptive behaviours may consider adapting the approach of utilising individual participant data. Conclusion The present review is, to our knowledge, the first to comprehensively assess inhibitory control performance in children with ODD/CD compared to healthy controls, children with ADHD, and ODD/CD + ADHD. Furthermore, this meta-analysis has taken into consideration ADHD symptomatology between groups. Importantly, in line with contemporary models of psychopathology, this review has highlighted the need to consider disruptive behaviour pathology from a dimensional perspective; which paves the way for future research and potential implications for diagnosis and treatment. While the understanding of the broader aetiological framework of disruptive behaviours is limited, the present meta-analysis found that inhibitory control decits contribute to the development of disruptive behaviours. Electronic supplementary material The online version of this article (https:// doi. org/ 10. 1007/ s10802- 020- 00713-9) contains supplementary material, which is available to authorized users. Acknowledgements The rst author is a recipient of a Grith Univer- sity postgraduate research scholarship. The authors would like to thank Dr David Reilly, Dr Sheri Madigan, and Mr Daniel Sullivan for their feedback on the analyses of this paper. Author Contributions All authors contributed to the study conception and design. Material preparation and data collection were performed by Mikaela Bonham and Olivia Elvin. Analyses were performed and Research on Child and Adolescent Psychopathology 1 3 the rst draft was written by Mikaela Bonham. 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Research on Child & Adolescent Psychopathology is a copyright of Springer, 2021. All Rights Reserved.

Treatment Sequencing for Childhood ADHD: A Multiple-

Randomization Study of Adaptive Medication and Behavioral

Interventions

William E. Pelham Jr.1, Gregory A. Fabiano2, James G. Waxmonsky3, Andrew R. Greiner1,

Elizabeth M. Gnagy1, William E. Pelham III4, Stefany Coxe1, Jessica Verley2, Ira Bhatia2,

Katie Hart1, Kathryn Karch2, Evelien Konijnendijk2, Katy Tresco2, Inbal Nahum-Shani5, and

Susan A. Murphy5

1Florida International University

2State University of New York at Buffalo

3Pennsyvania State University

4Arizona State University

5University of Michigan

Abstract

Objective—Behavioral and pharmacological treatments for children with ADHD were

evaluated to address whether endpoint outcomes are better depending on which treatment is

initiated first, and, in case of insufficient response to initial treatment, whether increasing dose of

initial treatment or adding the other treatment modality is superior.

Methods—Children with ADHD (ages 5–12, N = 146, 76% male) were treated for one school

year. Children were randomized to initiate treatment with low doses of either (a) behavioral parent

training (8 group sessions) and brief teacher consultation to establish a Daily Report Card or (b)

extended-release methylphenidate (equivalent to .15 mg/kg/dose bid). After 8 weeks or at later

monthly intervals as necessary, insufficient responders were rerandomized to secondary

interventions that either increased the dose/intensity of the initial treatment or added the other

treatment modality, with adaptive adjustments monthly as needed to these secondary treatments.

Results—The group beginning with behavioral treatment displayed significantly lower rates of

observed classroom rule violations (the primary outcome) and parent/teacher ratings of

oppositional behavior at study endpoint and tended to have fewer out-of-class disciplinary events.

Further, adding medication secondary to initial behavior modification resulted in better outcomes

on the primary outcomes and other measures than adding behavior modification to initial

medication. Normalization rates on teacher and parent ratings were generally high. Parents who

began treatment with behavioral parent training had substantially better attendance than those

assigned to receive training following

medication.

Correspondence should be sent to: William E. Pelham, Jr., Professor of Psychology, Florida International University, 11200 SW 8th St
AHC1 146, Miami, FL 33199, 305-348-3002; fax 305-348-3646.

HHS Public Access
Author manuscript

J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July

01.

Published in final edited form as:
J Clin Child Adolesc Psychol. 2016 ; 45(4): 396–415. doi:10.1080/15374416.2015.1105138.

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Conclusions—Beginning treatment with behavioral intervention produced better outcomes

overall than beginning treatment with medication.

Keywords

Behavioral treatment; pharmacological treatment; ADHD

It is well established that evidence-based treatment for attention-deficit/hyperactivity

disorder (ADHD) includes medication with psychostimulants (Conners, 2002; Greenhill,

Pliszka, Dulcan, & the Work Group on Quality Issues, 2002) and behavioral interventions

(Pelham & Fabiano, 2008; Evans, Owens & Bunford, 2014; Fabiano et al., 2009). These two

modalities of treatment have been studied for decades, both separately and in combination.

Even so, disagreements remain among professionals regarding which treatment modality is

preferable, as well as how treatment for ADHD should begin. Some recommend beginning

medication immediately and supplementing with additional medication when necessary

(AACAP Work Group on Quality Issues, 2007). Others recommend beginning with

psychosocial treatments and adding medication if those treatments are insufficient (APA

Working Group on Psychoactive Medications for Children and Adolescents, 2006). Others

recommend starting with both treatments simultaneously (http://www.chadd.org). Most

recently, the American Academy of Pediatrics recommended each of the above strategies for

different ages of children (Subcommittee on Attention-Deficit/Hyperactivity Disorder,

Steering Committee on Quality Improvement and Management, 2011). However, the

research base upon which these recommendations have been made is scant and limited in

important ways (see for example Fabiano, Schatz, Aloe, Chacko, & Chronis-Tuscano, 2015).

In contrast to the hundreds of studies evaluating stimulants and behavioral interventions

separately, only a handful of randomized controlled trials (RCT) have compared medication,

behavioral treatment, and their combination, and each of these trials has limitations. A

common feature in the existing studies is that they have used fixed doses—typically

relatively high doses—of each treatment. For example, the largest and best-known RCT of

comparative treatments for ADHD is the MTA (MTA Cooperative Group, 1999a), which

used “optimal” dosing of medication (e.g., medication at school, evenings, and weekends)

compared with a package of intensive behavioral treatments (parent training, summer

treatment program, extensive teacher consultation, a classroom aide in school), and a

combined condition that added the two high-dose treatments and began them

simultaneously. The high-dose behavioral treatment was complex and costly, whereas the

high-dose medication treatment had adverse effects on growth. The results of the MTA vary

considerably based on the measure, individual differences, setting, timing of assessments,

length of follow-up, and interpretation (e.g., MTA Cooperative Group, 1999a, 1999b, 2004;

Molina et al., 2009; Pelham, 1999; Pelham et al., 2000; Owens et al., 2003; Swanson et al.,

2007), suggesting that additional finely-tuned investigations with different doses and

sequences of treatments are necessary to clarify relative effects of the two major, evidence-

based treatment modalities.

More recent research has used both within-subject and RCT designs to evaluate multiple

doses of medication in different combinations with varying doses of behavioral treatments

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(Fabiano et al. 2007; Pelham et al., 2005; Pelham et al., 2014; Pelham et al., under review).

These studies have consistently found that intensive behavior modification produces acute

effects similar to relatively high doses of medication, but that low doses of both treatments

also maximize response in some but not all children. Further, these studies show that

combining low-dose medication with low-intensity behavioral interventions produces

equivalent effects to those of high-dose/high-intensity unimodal treatments for the majority

of children but with lower side effects, high parental satisfaction, and less complex

behavioral interventions. Side effects of stimulants increase with escalating dose and

duration of exposure (Barkley et al. 1990; Pelham et al, 1999; Stein et al, 2003, Swanson et

al., 2007). Therefore, adding behavioral interventions that reduce medication dose should

improve the tolerability of medication treatments. These studies have provided much-needed

information regarding the relative effects of different doses of medication and behavior

modification. However, they were implemented in an analogue summer treatment program

setting and thus do not directly address whether low doses of either modality or their

combination would be sufficient for many children in

community settings.

Moreover, no studies in the literature have systematically varied and compared the sequence
in which the two evidence-based modalities for ADHD are implemented. Medication is the

most commonly employed intervention and often the only intervention used in practice

(Epstein, et al., 2014; Visser et al. 2014) even in young children where professional

guidelines recommend starting with behavioral treatments (Subcommittee on Attention-

Deficit Hyperactivity Disorder, 2011). Psychiatric guidelines endorse optimizing dose at

home and school and using multiple medications prior to adding behavioral treatments

(AACAP Work Group on Quality Issues, 2007). When medication is implemented at this

high intensity level, there is less need for behavioral interventions, so the opportunity does

not exist to discover whether some or most children would do well with behavioral

interventions alone. For example, 75% of the individuals in the MTA behavioral treatment

group remained without medication during the year of treatment, and, for the majority of

those, for years afterward (MTA Cooperative Group, 1999a, 2004, Molina et al., 2009). This

implies that many children might not need medication if behavioral treatments were

employed first. Further, the majority of children in the medication management group

needed additional treatment during the 14-month treatment period, but only medication

could be used in this condition, and maintaining the initial medication effect required a 25%
increase in dose during the year of treatment (Vitiello et al., 2001). In the combined

treatment group, an adjustment to the classroom intervention—most often the Daily Report

Card (DRC)—had to be made before medication dose could be increased, and that

procedure reduced the need for increased doses of medication (Vitiello et al., 2001). The

simultaneous introduction of conditions in the combined treatment group in the MTA means

that it is not possible to evaluate whether a behavioral intervention employed before
medication would have prevented the need for medication or reduced the dosage needed.

Thus, a significant limitation of existing ADHD treatment studies is that questions regarding

sequencing, dosing, and combining treatments in natural settings have not been

systematically explored. In contrast to this body of research, treatment decisions in practice

are ongoing, based on the child’s impairment and response to intervention, and typically

provided initially at low “doses” that are escalated only if necessary. There are two crucial

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decision points in treating a child with ADHD: (1) which treatment should be implemented

first? and (2) what should be done if the child does not respond adequately to that initial

treatment? For example, if a child begins treatment with medication and fails to respond,

there are two possibilities – increase the medication dose or add behavioral treatment. These

decision points have many implications with regard to tolerability/side effects, treatment

cost, and treatment efficacy, yet no studies have systematically evaluated such treatment-

sequencing questions for ADHD.

Adaptive treatment strategies have been gaining recognition as a strategy for preventive

interventions and management of chronic disorders (Murphy, 2005; Collins, Murphy, &

Bierman, 2004). In an adaptive approach, different dosages of treatment are provided

differentially to individuals across time in response to decision rules that are based on

individual characteristics. The major advantage of adaptive treatment designs is that they

mimic what happens in typical practice where treatments are often modified or enhanced,

but they retain controlled procedures, dosages, and rules to ensure replicability. Adaptive

approaches have previously been used with comprehensive services of the type that are used

for children with ADHD (for examples see Conduct Problems Prevention Research Group,

1999a, 1999b). Thus, in the present investigation, we employed a research design that has

been recommended for developing and comparing adaptive strategies, a sequential multiple
assignment randomized trial (SMART; Murphy, 2005; Lavori & Dawson, 2000). In such

trials, individuals are randomized at multiple decision points to produce each treatment

strategy, combinations of which can be analyzed because they have been assigned by

randomization.

The current study was undertaken to address the limitations in the existing treatment

literature for children with ADHD with regard to treatment decisions and sequencing. A

SMART design was used to compare the results of various treatment decisions that included

behavioral and/or pharmacological interventions and their combination that can be widely

applied in clinical practice. Starting with low doses, treatments were conducted over an

entire school year in children’s school and home settings and adapted monthly within setting

based on response and need for additional intervention. End-of-study outcomes were

measured on objective classroom observations of behavior and parent/teacher ratings to

determine the relative benefits of the treatments and their sequences.

Within this design we were able to examine three important clinical questions/aims. First

(Aim 1): does it produce better outcomes on endpoint objective classroom measures and

parent/teacher ratings to initiate treatment with a low dose of (a) pharmacological

intervention with a stimulant drug or (b) behavioral intervention (group parent training and a

DRC at school)? Second (Aim 2), what is the most effective treatment protocol, or pattern of

initial treatment and conditional secondary/adaptive treatment (e.g., BM: behavioral

followed by medication in the event of insufficient response) among the four that we

employed (BM, Behavioral-Behavioral (BB), Medication-Behavioral (MB), and

Medication-Medication (MM)? Third (Aim 3), in the event of insufficient response to one of

the initial treatments, are endpoint results improved more by increasing the dose of that

modality (e.g., adding secondary/adaptive B to initial B (B then B) when necessary) or

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adding treatment with the other modality (e.g., adding secondary/adaptive M to initial B (B

then M)?

Methods

Participants

One hundred, fifty-two children with ADHD, between the ages of 5 and 12, participated.

Participants were recruited in three cohorts of approximately 50 each via radio

advertisement; direct mail; and referrals from schools, physicians and mental health

providers. Recruitment occurred during the spring and summer of 2006, 2007, and 2008,

with treatment commencing in September of each year and continuing throughout the school

year.

Exclusionary criteria included: (1) Full Scale IQ below 70; (2) history of seizures or other

neurological problems and/or medication to prevent seizures; (3) history of other medical

problems for which psychostimulant treatment may involve considerable risk; (4) childhood

history or concurrent diagnosis of pervasive developmental disorder, schizophrenia or other

psychotic disorders, sexual disorder, organic mental disorder, or eating disorder; (5) lack of

functional impairment; and (6) placement in special education classrooms.

After screening and informed consent, parents and teachers completed a number of

instruments to determine diagnosis and study eligibility. To determine ADHD diagnosis,

parents and teachers completed the Disruptive Behavior Disorders (DBD) Rating Scale

(Pelham, Gnagy, Greenslade, & Milich, 1992). The DBD RS is a list of the DSM symptoms

of ADHD, oppositional-defiant disorder (ODD) and conduct disorder (CD), updated for

DSM-IV, and rated as not at all, just a little, pretty much, or very much. In addition, parents

completed a semi-structured DBD interview consisting of DSM-IV symptoms of ADHD,

ODD, and CD with supplemental situational probes (available from the first author). Parents

and teachers completed the Impairment Rating Scale (IRS: Fabiano et al., 2006), which asks

parents and teachers to evaluate on a six point Likert scale the degree to which a child is like

a typical child and needs no treatment or has extreme problems that definitely require

treatment or special services in five areas of function—relationship with parents/teachers,

relationships with peers/siblings, academic progress, general classroom/family functioning,

and overall functioning. Two clinicians independently reviewed all screening instruments

and made diagnoses based on the DSM-IV rules, counting a symptom as present when

endorsed by either teacher or parent (pretty much or very much on the DBD or parent

interview). Impairment also had to be present in any domain, as indicated by cutpoints on

the Impairment Rating Scale (IRS; Fabiano et al., 2006). In case of disagreement, a third

clinician reviewed the file to determine final diagnosis. Eighty percent of the children met

criteria for ADHD-Combined Type, with 15% Predominately Inattentive and 5%

Predominately Hyperactive/Impulsive. Comorbid rates of ODD and CD are shown in Table

1, along with demographic and descriptive information. None of the ADHD diagnoses and

only 2% of the ODD and CD cases required a third reviewer to confirm diagnosis.

The sample size was determined using data from our previous study in a controlled setting

(Fabiano et al., 2007; Pelham et al., 2014) to estimate effect sizes for the first-stage

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treatments used in this study. These calculations determined that a sample size of 150 would

result in at least 80% power for testing first-stage differences of at least 0.5 standard

deviations when testing at a 0.05 level of significance. Recruitment and participant flow are

illustrated in Figure 1. Six participants withdrew prior to the end of the study; 146 children

completed the study assessments (96%). Three families withdrew because they did not wish

to use medication and three withdrew because teachers refused participation upon initial

contact after the family had been randomized. In the context of this multiple-randomization

design, early withdrawal results in missing data on the group membership variable (i.e.,

responder versus non-responder). Although there are methods that can address this particular

challenge (e.g., Shortreed, Laber, Scott-Stroup, Pineau, & Murphy, 2014), the subsequent

analyses utilized only the 146 completers despite the use of multiple imputation to address

missing data.

Design

Figure 2 illustrates the study design. Participants were randomly assigned to one of two

initial treatments that were initiated at the beginning of the school year: low-dose medication

for school hours only—Medication First (MedFirst) —or low-intensity clinical behavioral

intervention consisting of weekly behavioral parent training groups (BPT) and a school

consultation to establish a Daily Report Card (DRC: Jacob & Pelham, 2000; available at

http://ccf.fiu.edu; Volpe & Fabiano, 2013)—Behavior First (BehFirst). Eight weeks of

treatment were then provided to allow for sufficient time to implement behavioral treatments

and medication and to measure their impact, after which each participant’s response was

measured according to the procedures described below. If a child experienced continued

impairment in the school and/or home setting—that is, insufficient response to the initial

treatment—then a second randomization occurred. At this point, one of two treatment

strategies was employed in the setting(s) where impairment was present: (1) increase the

dose/intensity of the initial treatment or (2) add the other treatment for a combined treatment

modality. Children who responded to the initial treatment condition were maintained on that

condition and monitored monthly; if their performance deteriorated at any time during the

school year, then the second treatment randomization occurred at that time. Children’s

progress continued to be monitored and their secondary treatment condition was tailored

adaptively (initial treatment was not tailored). For example, a child who began treatment

with a 10-mg dose of medication and was re-randomized to receive behavioral treatment

stayed on the initial 10-mg dose for the remainder of the school year, and subsequent

changes were made only to the adaptive behavioral part of the treatment. Treatments,

evaluations of response, and treatment adjustments were made independently for the home

and school settings, which afforded independent evaluations of need for treatments,

adherence, uptake, and effectiveness at home and at school.

Assessing need for additional treatment—Each month, parents and teachers

completed ratings on a study-specific version of the IRS. The IRS was modified to ask

whether, given the treatment currently in place, the child needed additional treatment, with

responses ranging from 1 (definitely not) to 5 (definitely yes). If a rater responded probably
yes or definitely yes in any domain, a study staff member called the rater to ask follow-up

questions about the child’s impairment to ascertain whether the rating indicated true need for

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additional services, and to ensure that the impairment could be addressed with the available

treatments (e.g., clinicians ruled out that a significant life event may have triggered a

temporary increase in problem behavior or that comorbid learning problems may have

accounted for impairment in academic progress).

As an objective measure of response to intervention in the school setting, teachers also kept

records from an individualized target behavior evaluation (ITBE; Pelham, Fabiano &

Massetti, 2005). The ITBE is sensitive to treatment effects, can be implemented by general

education classroom teachers, and is individualized to children’s areas of impairment

(Fabiano, Vujnovic, Naylor, Pariseau, & Robins, 2009; Fabiano et al., 2010). During the first

few weeks of school, a study case manager met with each child’s teacher to establish target

behaviors (e.g., work completion, complying with teacher directions, behavior toward peers)

and criteria for what the teacher considered success on that target behavior. ITBE goal

attainment percentages were computed across class periods each day, and weekly averages

were calculated for evaluation. ITBE results were not shared with children or parents for

children in the MedFirst group. For children in the BehFirst and adaptive behavioral groups,

the ITBE doubled as a DRC and was sent home to parents, who provided contingent

consequences at home.

At the 8-week point and monthly thereafter, the study team met to discuss each case. If

monthly IRS ratings indicated impairment, the study team ensured that the impairment was

related to an appropriate target of study treatments. Two clinicians who were not directly

involved in the child’s treatment and were unaware of the initial treatment condition were

required to agree that additional treatment was necessary based on the teacher or parent IRS

before the child could be rerandomized. In the school setting, ITBE performance was

simultaneously evaluated; if weekly averages consistently fell below 75%, and need for
additional treatment was also indicated on the IRS, then additional treatment was

considered. Finally, if a child was in immediate danger of class failure or school suspension,

these factors were taken into account in treatment decisions.

For the children who were rerandomized, monthly treatment decisions were made regarding

additional dose increases or adjustments to the behavioral treatment. These decisions were

made using the same criteria as for initial response. Treatment recommendations were

tailored to specific domains of impairment as described below. Parents were able to decline

treatment recommendations for medication or additional behavioral services, but

recommendations were reoffered monthly if indicated. All treatment recommendations, the

reasons for them, and records of treatments received were documented.

Treatment Descriptions

Table 2 lists the components of the low and high dose medication and behavioral treatments.

Children were initially randomized to a dose of behavioral or medication treatment, with

additional treatment added, if indicated, based on a second randomization (see Figure 2).

Initial Treatments—For children assigned to the BehFirst condition, parents received an

8-session, group parent training program using the Community Parent Education Program

which has been extensively used with ADHD children (COPE; Cunningham, Bremner, &

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Secord-Gilbert, 1998); children participated in concurrent group social skills training

sessions, modified after a recreational period in the Summer Treatment Program (STP;

Pelham et al., 2010). Prior to the first group parenting session, parents received an individual

session to establish a home reward system for the DRC. The case manager also conducted

three brief consultation visits with the child’s teacher regarding standard classroom

management strategies. This included an initial review of the teacher’s classroom

management practices, discussion of basic classroom management, including praising

appropriate behavior, planned ignoring, and appropriate commands, as well as procedures

related to implementing a DRC. DRCs were sent home each day and parents provided daily

and weekly rewards for good performance at school. Following the initial 8-week treatment

period, monthly parent-training booster sessions with a focus on maintenance and problem-

solving were offered for the remainder of the school year. The case manager also

communicated with the teacher each month regarding adjustments to the DRC and the basic

classroom management interventions that were in place.

For children assigned to the MedFirst condition, a dose equivalent to 0.15 mg/kg/dose b.i.d.

of immediate release methylphenidate was calculated. In order to separate home and school

settings for assessment and interventions, an 8-hour extended release preparation of

methylphenidate (MPH) was used for the school setting only. For most children (92%), this

was 10 mg per day of the extended release MPH preparation; for the remainder, their initial

dose was 20 mg daily. School doses were administered by parents in the morning prior to

school, and home meds were administered after school and on weekend mornings. The 0.15

mg/kg dose was selected based on data from controlled studies (Fabiano et al. 2007, Pelham

et al., 2005; Pelham et al., 2014) showing significant effects over placebo that are similar to

a low intensity behavioral intervention with very good tolerability. Side effects were

monitored weekly for the first two weeks of medication administration and monthly

thereafter; spontaneous reports of side effects were also collected. Any time a child

experienced moderate or severe side effects, the study physician made dosing adjustments if

necessary. The case manager also adjusted the ITBE for children in this group as needed

monthly based on teacher report.

Secondary (Adaptive) Treatments—For children who began with behavioral

treatment and were rerandomized to receive secondary/adaptive behavioral treatment (B-

then-B), more intensive standard behavior management procedures were implemented first

to address individualized areas of impairment. At school, these included school-based

rewards for DRC performance, classwide reward contingency systems, intensive classroom-

based contingency programs administered by the teacher or a paraprofessional, and time-out

procedures. Home-based DRCs and individual parent-training sessions were introduced in

the home setting. Other interventions were then added to address specific areas of child

impairment (See Table 2).

For MedFirst children who began with medication and then were rerandomized to

secondary/adaptive behavioral intervention (M-then-B), the standard initial behavioral

treatments were implemented first (i.e., group parent training, DRC consultation). After

eight weeks, the additional services described above were added according to the child’s

continued impairments and need for tailored behavioral treatments.

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For children who began with medication treatment and were rerandomized to receive

secondary/adaptive medication (M-then-M), adjustments were made in two ways. First, the

morning dose of the extended release MPH preparation given on school days could be

increased if problems continued at school. Second, an after-school dose of immediate release

MPH could be added to the child’s regimen if home behavior or homework completion were

impaired (cf. Greenhill et al., 1996). In addition, MPH could be added for weekends. Parents

also had the option of switching to a 12-hour formulation if the criteria for additional

treatment were met in both settings.

For children who began with behavioral treatment and were rerandomized to receive

secondary/adaptive medication (B-then-M), medication could be added as above for school,

home, or both settings. Performance was evaluated monthly and adjustments were made

taking into account impairment level and side effects.

Primary Dependent Measure

Classroom rule violations—As it is commonly regarded as the gold standard in

assessments of treatment outcome for ADHD children in school settings, we used objective

observation of student behavior in the classroom context as our primary dependent measure

(Fabiano et al, 2009; Pelham, Fabiano, & Massetti, 2005). Every 4–6 weeks, independent

observers visited the children’s classrooms and conducted 40-min. direct observations

during academic tasks. Observers used the Student Behavior/Teacher Response code

(available from first author), which includes observations of children’s rule-breaking

behaviors (i.e., disrespect toward others, noncompliance with teacher requests, disrupting

others, leaving seat without permission, inappropriate use of materials, speaking out without

permission, and off-task behavior) and the teacher’s response to those behaviors (e.g.,

ignoring, providing a reprimand, providing a consequence; Vujnovic et al., 2014). Child

behaviors were coded independently of teacher responses and were coded even if the teacher

did not observe the behavior. Observers watched the entire class and coded behaviors

exhibited by the target child and classmates. Classmates were observed anonymously and

were not identified to the observer. The average number of behaviors exhibited by

classmates was computed to produce a classroom comparison rate used as a covariate in

analyses. The final observation of the school year was used in endpoint analyses because it

corresponded with the time interval during which parent and teacher endpoint ratings were

collected

In order to enhance reliability, observers were required to memorize operational definitions

of behavior categories and completed a training session consisting of role-plays, practice

observations, and classroom observations with an experienced observer. For 21% of the

classroom observations, a second trained observer accompanied the primary observer and

conducted an independent reliability observation of the same classroom. Reliability of the

observations was high, with a correlation of 0.91 (p<0.01), and a mean difference of 2.3 (SD = 2.8; range = 0–17) for the total classroom rule violations. These figures are consistent with

those from previous studies using the same observational code (e.g., Fabiano et al., 2010).

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Secondary Dependent Measures

Number of out-of-class disciplinary events—Teachers kept daily records of out-

of-class disciplinary events (e.g., being sent to the principal’s office). The number of

reported events was summed over the length of the school year for subsequent analysis.

Parent and teacher ratings—At endpoint, parents and teachers completed the DBD

RS and the Social Skills Rating System (SSRS; Gresham & Elliott, 1989). These measures

have been widely used in studies of ADHD and have published psychometric information.

Tracking of Treatment Fidelity

Attendance records were kept for all treatment sessions, and clinicians recorded all meetings

and contacts with teachers and parents. Medication dispensing records were kept. Parents

returned all unused pills at each medication visit, and pill counts were conducted to

determine the number of pills used.

To ensure fidelity with the behavioral treatments, all treatment components were manualized

and procedural checklists were developed for all parent and teacher sessions. Clinicians met

weekly with supervisors to review records of their sessions, and supervisors provided

feedback as necessary. At each classroom observation, the observer recorded whether or not

the teacher implemented the prescribed behavioral management procedures during the

observation period.

Missing Data Handling

Missing values were minimal across all study variables and all participants. Outcomes

ranged from 0 to 14% missing: classroom rules violations (98% complete), out-of-class

disciplines (97%), teacher DBD rating (99%), teacher SSRS rating (99%), parent DBD

rating (90%), parent SSRS rating (86%), and final medication doses (100%). At the

participant level, 125 of 146 (86%) participants had complete data for all of the analyzed

outcomes. We used multiple imputation to ensure unbiased estimates, assuming the data to

be Missing at Random (MAR). Here MAR is a plausible assumption given the inclusion of a

large number of covariates, including baseline measures of outcome variables; measures’

values at earlier waves are typically the best predictors of missing values at later waves in a

longitudinal design.

Imputation—In order to accommodate the non-normal distributions of many relevant

variables, we implemented a chained equations approach in R 3.1.3 (R Core Team, 2015)

using the mice package (v2.22; van Buuren & Groothuis-Oudshoorn, 2011) extended by the

countimp package for imputing count variables (v1.0; Kleinke & Reinecke, 2013). As

methodologists recommend an inclusive strategy (Collins, Schafer, & Kam, 2001), the

imputation model included approximately 50 variables: all the variables in the subsequent

analyses, all the sample characteristics listed in Table 1, and baseline measures of outcomes

wherever available. Distributions of all imputed variables were inspected and each was

modeled using normal, predictive mean matching, negative binomial, logistic, and

multinomial regressions, as indicated. Due to the large number of items and their high

correlations (i.e., multiple items from the same measure), imputation occurred at the level of

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the scale rather than at the level of the item. One hundred imputed data sets were created,

following recent recommendations that using larger number of imputations can minimize

simulation error (White, Royston, & Wood, 2011).

Analysis and pooling—All subsequent analyses were conducted in SAS 9.3. Analysis

was completed separately on each of the 100 imputations according to the procedures

described in subsequent sections. SAS 9.3 PROC MIANALYZE was used to combine

estimates across imputations; all reported estimates represent these combined (or pooled)

estimates.

Analytic Plan

Our analyses largely parallel those described by Nahum-Shani and colleagues (2012); we

direct readers to that article for more details about SMART design analyses. In the present

study, the analysis of treatment outcome data included a series of comparisons to test

different treatment decisions. Each comparison is described below.

Main effect of initial treatment assignment on endpoint outcomes (Aim 1)—
End-of-treatment outcomes of those that started with medication (MedFirst group) and those

that started with behavioral treatment (BehFirst group) were compared using regressions

with group membership as a predictor in order to examine whether the initial treatment

modality impacted outcome. In addition, survival analyses were conducted to determine

whether the groups differed in the need for additional treatment and the length of time

before children needed additional treatment.

Pairwise comparisons among SMART-embedded treatment protocols on

endpoint outcomes (Aim 2)—Second, outcomes were compared across each of the four

treatment protocols naturally embedded in the SMART design—BB, BM, MB, and MM.

The first letter denotes that protocol’s initial treatment (first-stage treatment in Nahum-Shani

et al, 2012) and the second letter denotes that protocol’s secondary/adaptive treatment

(second stage treatment in Nahum-Shani et al, 2012), to be implemented in the event of

insufficient response to the initial treatment. For example, the BM protocol entailed starting

the participant with behavioral treatment and then adding medication if and only if there was

insufficient response. It is important to note that the protocols do not reflect the actual

treatment received, but rather the set of rules followed to assign treatment at both stages. For

example, a child who responded to Behavior First and is therefore never rerandomized to

receive secondary/adaptive treatment is included in analyses of the BM protocol even though

he did not receive medication. This idea of being consistent with a particular embedded

protocol is a subtle but important aspect of the SMART design that is discussed in detail

elsewhere (Nahum-Shani et al., 2012). In the present analyses, we used an effects coding

scheme and generalized estimating equations to achieve all the pairwise comparisons of

protocols in a single model using SAS PROC GENMOD with robust standard errors, as

described in the appendices of Nahum-Shani et al. (2012). We also gave weights of 2 to the

responders to first-stage treatment and weights of 4 to the insufficient responders in order to

account for the systematic undersampling of the latter in each protocol due to the second re-

randomization (Nahum-Shani et al., 2012).

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Comparison of endpoint outcomes for secondary/adaptive treatments given

insufficient response to initial treatment (Aim 3)—Third, supplemental comparisons

were performed within each of the initial treatment arms to determine whether it is better to

augment (i.e., increase the dose of) that treatment or add the other treatment, given

insufficient response to an initial intervention. Thus, responders to the initial treatments were

excluded from these comparisons. These analyses consisted of regressions with group

membership as a predictor that compared (1) B-then-B with B-then-M and (2) M-then-M

with M-then-B.

Normalization rates—Finally, we used the procedure reported in Swanson et al. (2001)

to evaluate normalization of functioning on teacher and parent ratings at the study endpoint.

A score of 1.0 or lower on an aggregate of ADHD and ODD items from the DBD Rating

Scale was used to define normalization. Teacher and parent reports were examined

separately due to the separation of interventions across settings.

Count outcomes—Two dependent variables were counts: observed classroom rule

violations and number of out-of-class disciplinary events. Count outcomes often violate the

assumptions of linear (OLS) regression, so we adapted the SMART analysis procedure to

incorporate negative binomial regression, a robust approach to modeling count outcome

variables (Coxe, West, & Aiken, 2009). Negative binomial regression is related to the more

well-known Poisson regression, but relaxes some assumptions of Poisson regression that are

typically not met (i.e., equidispersion). As with the continuous outcomes, SAS PROC

GENMOD was used for these analyses, with the addition of the negative binomial modeling

and incorporating the OFFSET option to adjust for individual differences in the length of

observation intervals. In the observed classroom rule violations analyses, average peer rule

violations per hour was included as a covariate to control for the general level of disruptive

behavior in each classroom.

Results

Need for Additional

Treatment

In the school setting, 67% of the children who began treatment with behavioral interventions

required additional treatment by the end of the school year compared with 47% of the

children who began the school year receiving a low dose of medication (odds ratio or

OR=2.23). Survival analyses indicated a significant group difference; Breslow χ2=7.4, p < .

01.

In the home setting, there was no difference in rate of rerandomization for BehFirst (82%)

and MedFirst (88%) groups; OR=0.63. Almost all children met criteria for additional

treatment in the home setting regardless of initial treatment.

Endpoint Classroom Observations

Tables 3–6 display the results of analyses for classroom rules violations as well as

subsequent outcomes. Comparisons of initial treatment strategy (BehFirst vs. MedFirst)

revealed a significant difference on classroom rule violations, as illustrated in Figure 3.

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Children who began treatment with behavior management exhibited significantly fewer rule

violations per hour than children who received MedFirst (incidence rate ratio or IRR=0.66,

p<.01). Pairwise comparisons of the four treatment protocols revealed several significant

differences (Figure 3). The BB protocol resulted in fewer rule violations than the BM

protocol (IRR=0.78, p=.054), the MM protocol (IRR=0.56, p<.01), and the MB protocol

(IRR=0.50, p<.001). In addition, the BM protocol resulted in fewer rules violations than the

MB protocol (IRR=0.65, p<.01). For insufficient responders to first-stage behavioral

treatment, increasing the dose with second-stage behavioral treatment resulted in

significantly fewer violations than did adding medication (IRR=0.71, p<.05). For insufficient

responders to first-stage medication treatment, there were no significant differences between

second-stage treatments.

Out-of-Class Disciplinary Events

Comparisons of initial treatment strategy revealed a trend wherein the BehFirst group

displayed fewer out-of-class disciplinary events than the MedFirst group (IRR=0.52, p<.10,

Figure 3). Pairwise comparisons of the four treatment protocols indicated that (Figure 3): the

BM protocol resulted in significantly fewer events than the MB protocol (IRR= 0.16, p<.

001) and the BB protocol (IRR=0.34, p<.05), and the MM protocol resulted in significantly

fewer events than the MB protocol (IRR=0.34, p<.10). For insufficient responders to initial

behavioral treatment, adding medication trended toward resulting in significantly fewer

events than increasing the dose of behavioral treatment (IRR=0.30, p<.10). For insufficient

responders to first-stage medication treatment, increasing the dose with medication

treatment trended toward resulting in fewer events than did adding behavioral intervention

(IRR=0.27, p<.10).

Teacher Ratings

On teacher DBD ratings, no significant differences emerged for ADHD symptoms. For

ratings of oppositional/defiant behavior, the pairwise comparisons of the four treatment

protocols indicated a near significant advantage of the BM protocol over the MB protocol

(d=0.40, p=.06). The supplemental comparisons indicated that for insufficient responders to

first-stage medication, increasing the dose with second-stage medication trended toward

resulting in lower ratings of oppositional/defiant behavior than did adding behavioral

(d=0.61, p<.10).

For Total Social Skills score of the teacher SSRS, there was a trend toward advantage of the

BM protocol over the MB protocol (d=0.35, p<.10). Other comparisons were nonsignificant.

With regard to normalization of combined ADHD/ODD teacher ratings at endpoint, similar

numbers of the children assigned to MedFirst or BehFirst had mean DBD ratings of 1.0 or

less—69% and 78% respectively. Eighty-four percent of those who responded to first-stage

medication treatment met the normalization criterion, as did 92% of those who responded to

first-stage behavioral treatment. For those needing additional treatment, 63% of the M-then-

M group was normalized, compared to 61% of the B-then-B group, and 38% of the M-then-

B group compared to 80% of the B-then-M group.

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Parent Ratings

As with teachers, there were no significant differences on ADHD ratings in any

comparisons. For ratings of oppositional/defiant behavior, pairwise comparisons of the four

treatment protocols revealed a significant advantage of the BM protocol over the MB

protocol (d=0.56, p<.05) and BB protocol (d=0.38, p<.10), as well as a trend advantage of

the MM protocol over the MB protocol (d=0.40, p<.10). The supplemental comparisons

indicated that for insufficient responders to first-stage behavioral, adding second-stage

medication trended toward resulting in lower ratings of oppositional/defiant behavior than

did increasing the intensity of behavioral treatment (d=0.45, p<.10). Likewise, for

insufficient responders to first-stage medication, increasing the dose with second-stage

medication trended toward resulting in lower ratings of oppositional/defiant behavior than

did adding behavioral (d=0.46, p<.10). There were no significant differences in any

comparisons of the Total Social Skills score of the parent SSRS.

With regard to normalization of ADHD/ODD parent ratings at endpoint, 31% of the

MedFirst and 39% of the BehFirst, groups met criteria for normalization. Two-thirds of

those who responded to first-stage medication treatment met the normalization criterion, as

did 54% of those who responded to first-stage behavioral treatment. For those needing

additional treatment, 34% of the M-then-M group was normalized, compared to 30% of the

B-then-B group, and 18% of the M-then-B group compared to 40% of the B-then-M group.

Treatment Received

For those who began with BehFirst, 3% of the families declined parent training. Remaining

parents attended an average of 6 of the 8 group sessions (median=7, mode=8), 69% attended

an adequate dose of parent training (cf. MTA Cooperative Group, 1999), and 31% attended

at least one booster session after the initial parent training (see Figure 4). All scheduled

teacher meetings were completed, and DRCs were established for all but one child in the

BehFirst condition. For children in B-then-M, 13 parents (21%) declined the initiation of

medication.

At the initial 8-week assessment point, 9% of the MedFirst families had declined

medication. Of those who accepted medication, they were medicated on 97% of their school

days. For those in MedFirst rerandomized to the M-then-B group, 60% of these parents did

not attend any of the assigned group parent training sessions (mean=1.9, median=0,

mode=0); only 11% of these families received an adequate dose of parent training, and only

11% attended at least one booster session (Figure 4). At school, all required teacher

meetings were completed and school DRCs were established.

In the adaptive behavioral treatment arms (either M-then-B or B-then-B), 11 children

attended Saturday Treatment Program sessions, 3 received extra academic tutoring, and 13

received additional intensive interventions at school (e.g., the good behavior game initiated

as a classwide intervention) administered by the teacher or a paraprofessional as part of the

adaptive behavioral treatment condition. The remainder received either additional standard

teacher consultations to establish higher-intensity teacher-delivered consequences such as

school-based rewards and class-wide contingencies or individual parent sessions to improve

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parenting skills or establish more intensive parent-delivered interventions at home. Eleven

families assigned to behavioral treatment took medication outside of the protocol. Five of

these families used the medication for only 1–2 months before stopping the medication.

To determine whether beginning treatment with behavioral intervention would decrease the

dose of medication required for a child treated with medication in school, school-day dosing

in mg/kg/dose equivalent was compared for the two groups involving adaptive medication:

B-then-M and M-then-M at endpoint. At the end of the school year, 24% of the B-then-M

group was unmedicated at school compared with 5% of the M-then-M group (that is, parents

either did not start or elected to stop medication). Of those who were medicated, children in

B-then-M were taking significantly lower doses at school (M=0.21 mg/kg/dose, SD=0.10)

than M-then-M (M=0.28, SD=0.14), F(1, 70)=4.26, p<.05. At home, 39% of the M-then-M

group and 35% of the B-then-M group were unmedicated (parents either did not start or

elected to stop medication). For those who were medicated at home, doses were not

significantly different: (B-then-M: M=0.22, SD=0.10; M-then-M: M=0.21, SD=0.12).

Discussion

This study addressed three key questions: first (Aim 1), does it produce better outcomes on

objective classroom measures and teacher and parent ratings to begin treatment with a low

dose of (a) medication treatment or (b) behavioral treatment? Second (Aim 2), what is the

most effective treatment protocol, or pattern of first-stage treatment and conditional second-

stage treatment among the four imbedded SMART treatment protocols? Third (Aim 3), in

the event of insufficient response to a specific initial treatment, is it more effective to

increase the dose of that modality or add treatment with the other modality? All groups were

functioning relatively well at endpoint, as was expected given that two effective treatments

were compared. However, there were important differences in outcomes, as a result of the

initial treatment assignment and the protocol followed. Our findings provide the following

answers to the three questions/Aims noted above as follows:

1. Beginning treatment with a low dose of behavior modification resulted in

significantly lower rates of observed classroom rule violations and a trend for

out-of-class disciplinary events relative to beginning with a low dose of

medication.

2. The best of the four specific treatment protocols began with behavioral

treatment and then added medication in the event of insufficient response

(BM). The worst protocol began treatment with medication and added

behavioral treatment in the event of insufficient response (MB). The BB and

MM protocols produced outcomes in between these two and were often

comparable, though BB was superior to MB and MM on the primary outcome

variable.

3. In the event of insufficient response to initial behavioral treatment, increasing

the intensity of behavioral treatment (B-then-B) was significantly superior on

the primary outcome (classroom rule violations); adding medication (B-then-

M) had nominal advantages on several other outcome variables, two of which

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were trends. In the event of insufficient response to medication, increasing

dose of medication (M-then-M) was nominally superior to adding behavior

modification (M-then-B) on every measure with small to moderate effect sizes,

three of which were trends.

These results have clear implications for treatment for children with ADHD in mental

health, primary care, and school settings, and we discuss each in turn.

With regard to our first question, beginning school-based treatment with a low dose of

behavior modification (eight sessions of group parent training plus establishing a DRC at

school with home rewards) resulted in functioning in the school setting on key outcome

measures that was comparable to or better than beginning school-based treatment with a low

dose of stimulant medication. Notably, the low dose of behavior modification was a superior

starting strategy on the primary outcome measure, direct observations of classroom behavior

(66% as many rule violations), as well as the frequency of out-of-class discipline (54% as

many incidents). Although teacher ratings did not differentiate BehFirst from MedFirst,

ratings of oppositional behavior decreased by more than 50% from baseline in the BehFirst

group, and teachers rated 78% of children in the BehFirst group as normalized. For 33% of

the BehFirst children, the low dose of behavioral intervention was sufficient treatment in

school for the entire school year.

There were no differences between the groups in the numbers of children who needed

additional treatment at home, with more than 80% of both groups meeting criteria for

rerandomization (see discussion below). Similarly, there were no significant differences

between initial treatments on parent ratings of symptoms, oppositional behavior, or social

skills.

Interestingly, compared to the 33% who did not need additional school-based treatment in

BehFirst, nearly two-thirds more, 53% of children in the MedFirst initial assignment did

sufficiently well with the low dose of medication that they did not require additional

treatment. Further, a substantial portion of the children assigned to the BM embedded

protocol (24%) were not taking medication at endpoint—far more than the MB protocol. In

other words, although the combined treatment protocol (MB) within the MedFirst arm

contained more medicated children than did the combined treatment protocol (BM) within

the BehFirst arm, and although there were more initial responders in the MedFirst group

than in the BehFirst arm, MedFirst remained inferior to BehFirst as an initial treatment

condition.

These differences in the effects of the initial intervention may be linked to differences in

treatment uptake of parent training, as shown in Figure 4. Engagement in the parent training

groups and the booster sessions was dramatically reduced in the MedFirst families relative to

the BehFirst families. Indeed, most BehFirst parents attended the majority of BPT sessions

and received an adequate “dose” of BPT, while only a small minority of MedFirst families

who were assigned to BPT as a secondary/adaptive intervention attended BPT. In other

words, the provision of medication before the initiation of parent training was associated

with greatly reduced rates of engagement in parent training, and presumably worse

functioning at school and home. This finding parallels those reported in the STAR*D study

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of antidepressants for adults with major depression: 71% of adults that were insufficient

responders to SSRI treatment did not choose to pursue subsequent cognitive behavior

therapy (Wisniewski et al., 2007). Perhaps parents who began with behavioral treatment

were more motivated to engage because they had not already dealt with several of weeks of

problem behavior at school without having received the parenting toolkit provided in BPT.

Alternatively, perhaps parents who began with medication, which requires minimal effort

and time, were reluctant to participate in more effortful and time-consuming parent training,

a major portion of which is learning to provide home backup for the school DRC. It is

possible that the teachers were less engaged in second-stage behavioral interventions

following initial medication for the same reasons as parents. Other researchers have reported

difficulties with engagement and attendance in behavioral treatments for ADHD (see for

example Barkley et al., 2000). Additional research is necessary to elucidate the mechanisms

of this problem with treatment engagement. Whatever the mechanism, the clinical

implications are quite clear: if providers intend for the parents of ADHD children to receive
parent training and for teachers to provide “extra” classroom management for the child (i.e.,
for the child to receive multimodal treatment) but start treatment with medication, they
reduce the likelihood of engagement in behavioral treatment and thus negatively impact
treatment outcome. Unfortunately, standard practice among physicians is to provide

medication immediately rather than delay it until the completion of parent training. The

results of the present study suggest this is a poor strategy.

With respect to our second question/Aim—which of the four embedded treatment protocols

produces the best outcomes?—it should be noted that each initial assignment is associated

with two embedded protocols, specifically those that began with that particular modality

(i.e., for BehFirst, the BB and BM protocols). With this in mind, the comparisons of

treatment protocols suggest that the primary driver of the first-stage treatment main effect

was the discrepancy between the two combined protocols, BM and MB. As Table 4 shows,

the two protocols involving increasing the initial intervention (BB and MM) produced

generally comparable results (though BB was superior to MM on classroom observations),

but the BM combined-treatment protocol was significantly more effective than the MB

protocol. The former produced the best outcomes on all but two variables, while the latter

produced the worst outcomes on all but two variables. Children following a combined

treatment protocol that began with behavioral treatment were superior on measures of

classroom observations, disciplinary actions, and teacher and parent ratings of ODD. Not

surprisingly, the normalization rates on teacher ratings in the school setting in the children

receiving multimodal treatment was double for the B-then-M (80%) versus M-then-B (38%)

groups, whereas normalization rates for the B-then-B and M-then-M protocols were nearly

identical (61% and 63%). This finding illustrating the superiority of BM over MB has

substantial clinical implications, shedding light on how sequencing of intervention can

enhance (or inhibit) engagement within an evidence-based intervention. For example, the

results of the present study suggest that the failure to begin behavioral treatment before

medication may have contributed to the relatively small advantage of combined treatment to

medication alone in the MTA study.

With respect to our third question/Aim, how to best augment treatment given insufficient

response to initial treatment, Table 5 shows that a low dose of medication is a useful

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adjunctive intervention to add to initial behavioral treatment in the case of insufficient

response, as is increasing the intensity of behavioral intervention. Both additions were

helpful on a range of measures. In contrast, given insufficient response to medication,

increasing the dose of medication was superior across measures to adding behavioral

treatment (Table 6). This finding has important clinical implications. Once medication has

been employed, it appears that only a higher medication dose results in continued

improvement when more treatment is needed. As discussed above, the reasons for this may

be related to failure of parents (or teachers) to engage in behavioral treatments when they

follow medication. It should be noted that these comparisons involved small Ns, and power

to detect differences was limited. Further, a treatment regimen that includes only medication

is not a viable long-term treatment strategy for ADHD children, as it confers no long-term

benefit (Molina et al., 2009).

Furthermore, with regard to the secondary treatments involving adaptive medication,

children in the B-then-M group were taking significantly lower doses of medication at

school than children in the M-then-M condition. This finding indicates that beginning

treatment with behavior modification serves to decrease the necessary dose when medication

is used, which will result in lower levels of dose-related side effects (cf. Swanson et al,

2006).

Taken altogether, our results replicate and extend in the school-year environment what we

have reported in earlier studies conducted in analogue summer program settings (Fabiano et

al., 2007; Pelham et al., 2005; Pelham et al., 2014). Namely, a low dose of behavioral

treatment—in this study eight sessions of large group BPT and establishing a DRC at school

— is effective and sufficient for a substantial number of children with ADHD in school,

recreational, and home settings. Further, a low dose of medication (.15 mg/kg/dose b.i.d.)

was sufficient in the school setting for 53% of the children. Low doses of the two modalities

in combination were very effective for insufficient responders, but only when the behavioral
treatment came first. Neither our previous studies nor the MTA sequenced interventions, but

these results provide clear guidance about which sequence should be followed when

implementing combined treatment—BM rather than MB.

This is the first study to our knowledge that has addressed the effectiveness of such low-dose

interventions as a starting treatment for ADHD implemented in a community/school/clinic

setting. Low dose medication was sufficient in the school setting for a year for nearly half of

the children, but providing it first limited the effectiveness of additional behavioral treatment

when necessary. Further, the cost of the MedFirst condition and its protocols in the study
were far higher than BehFirst and its associated protocols (Page et al, this issue). Thus, the

study demonstrates that starting with a low dose of behavioral treatment and either

enhancing behavioral treatment or adding medication when necessary produces better

outcomes and is a far less costly approach to treatment for ADHD than starting with

medication (Page et al, this issue). Others have found increased side effects and reduced

tolerability as the dose and duration of medication increases (Barkley, McMurray,

Edelbrock, & Robbins., 1990; Stein et al., 2003; Pelham, et al, 1999; Swanson et al. 2006).

An adaptive approach in clinical practice that begins with low intensity behavior

modification and increases intensity or adds medication adaptively would appear to be the

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treatment approach of choice for children with ADHD. The MTA had previously shown that

medication dose escalations over time are less necessary when multimodal treatment is

being implemented compared to medication alone (Vitiello et al., 2001), but the present

results extend that finding to considerably lower and less costly (Page et al., this issue) doses

of both medication and behavioral treatment than employed in the MTA and most other

studies in the ADHD treatment field.

Importantly, the adaptive nature of the approach to behavioral intervention was effective in

this study in producing very positive outcomes with relatively low intensity interventions for

most children and enhanced interventions for a small subset. For example, the adaptive

behavioral treatments employed in the school setting were carried out by the general

education teacher without additional intervention staff in two-thirds of the cases, and in only

six cases was an intensive paraprofessional-based program implemented. In contrast, in the

MTA a half-day paraprofessional—a very costly intervention—was provided for nearly a

full semester for all participants regardless of need. The present results suggest that that was

unnecessary for the vast majority of the children. These findings illustrate the utility of the

adaptive treatment approach, in which children only receive the types and levels of treatment

they require based on individual impairment. As discussed in the companion to this paper

(Page et al., this issue), the BB protocol was the least costly of the four protocols and the

BM protocol a close second. Thus, our effectiveness results and costs show a far different

picture than presented in the only other comparative study of cost-effectiveness in the

ADHD literature (cf. Jensen et al, 2005). These findings have important implications for the

public health system and insurance companies with regard to treatment costs for ADHD and

call for a reassessment of federal, insurer, and medical society recommendations on

treatments for ADHD, which currently prioritize medication and limit the extent to which

behavioral treatments can be utilized.

Limitations

It is important to note that this was an effectiveness study carried out in the natural

environment and therefore strict experimental control over the behavioral interventions

could not be exerted. Given the prevalence of classroom management training in schools,

teachers in the medication–only group were no doubt routinely implementing behavioral

strategies to manage their classrooms, and parents in the medication-only group may have

been implementing behavioral practices such as time out. Some of the lack of differences in

behavioral treatments may thus be due to the natural presence of behavioral treatments in

school and home settings. In addition, it was not possible to collect measures of parents’ in-

home implementation of procedures such as rewarding the Daily Report Card. Furthermore,

although observers completed checks of treatment integrity and fidelity during their

observations, they were often unable to observe teachers’ implementation of specific

procedures that were to be implemented (e.g., tracking DRC targets and giving feedback to

the child). We therefore were unable to calculate specific data for the fidelity of teacher-

delivered interventions. In contrast, medication was provided with greater experimental

control, with dosing practices varying some from what is done in routine clinical practice in

order to systematically assess sequencing effects across settings. For example, initial

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medication treatments focused on school only, with evidence of objective impairment

required to be eligible for additional treatment at school or at home.

As noted above, many more children met criteria for additional treatment in the home setting

than in the school setting. In part this may be due to the initial treatment conditions. For

example, medication was initially provided only at school for MedFirst.. This approach

exposed those families to the impact of medication on their child at school and may have led

parents to rate their child as needing medication at home in order to obtain medication for

the home setting. The BPT program for BehFirst families was a brief, group-based

program–as opposed to the individually developed DRC for each child–and was sufficient

for only a subset of families. Others needed more individually-focused BPT, although the

amount required was relatively little except for a small subset of families. The lack of a

parallel, home-based DRC criterion (like the one used in the school setting) for additional

treatment also made it easier for children to meet criteria for additional treatment at home

relative to the criteria for allocating additional treatment at school. That is, parents simply

needed to indicate that their child was having problems and needed more treatment, whereas

teacher indication of need and ITBE target goal attainment rate below 75% were required at

school. This may also explain the somewhat contrary findings that BehFirst resulted in

superior outcomes relative to MedFirst, but the majority of children in the study met criteria

for rerandomization in the home setting. Future studies that utilize similar ITBE goal

attainment strategies in the home in addition to parent ratings may yield different outcomes

that are more similar to that obtained in the school setting in this study.

Another limitation relates to our study design, which included a maximum of two

randomizations per child. Ideally additional decision points might be included. For example,

in our protocol a child in need of adaptive second stage behavioral treatment could receive

ad lib treatment, as opposed to systematically limited, incrementally larger “doses” of

behavioral intervention, which might have been sufficient. Alternatively, for a child who is

still doing poorly after the first rerandomization, another opportunity to cross over to the

other treatment might be considered. For example, rather than a temporary classroom

paraprofessional, medication might have been considered at a third randomization for the

small number of children who require that level of assistance. This may be particularly true

in the case of nonadherence to the assigned treatments. The sample size required for

additional decision points precluded such considerations for the current study. Inclusion

criteria also required attendance within general education classrooms, so whether these

results generalize to self-contained special education classrooms is not known.

A final limitation was statistical power to address secondary aims. The study was fully

powered only for examination of the main effect of first-stage treatment (question/Aim 1).

We did not expect so many children to respond to first stage treatments and not need

additional treatment, so we did not plan for a larger N. Thus our power was reduced for

pairwise comparisons of the embedded treatment protocols (question/Aim 2), and then

further reduced for the comparison of treatments among insufficient responders

(question/Aim 3). Thus, numerous small to moderate effect sizes did not achieve statistical

significance but might have with a larger sample.

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Future Research

Finally, the adaptive methodology employed is promising for future studies of interventions

for ADHD in the pursuit of treatment tailoring for individual differences in functional

deficits. Replication of these results with a larger sample would afford better power and the

opportunity to investigate mediators and moderators for the treatment results that we

reported herein, including individual differences in comorbidity and impairment. For

example, why did parents whose children received medication first have such dramatically

reduced rates of uptake of parent training and associated poor outcomes relative to the other

protocols? Why did so many more children meet criteria for rerandomization at home

compared to school? Since combined treatment starting with behavior modification was so

effective, a natural follow-up question is how psychologists and other psychosocial mental

health and school-based providers can collaborate with M.D. prescribers in practice settings

to implement the conjoint strategies that were shown in this paper to be effective. Finally,

how might these interventions and approaches have to change to be effective with samples of

ADHD children both younger and older than our elementary-aged sample?

Clinical Implications

The results have direct relevance for clinical practice. The relatively low-dose-treatment

strategies that we employed are implementable in community mental health, primary care,

and school settings. The results suggest that practitioners should initiate treatment with low

doses of intervention, increasing intensity only when indicated. Many children will respond

sufficiently to low doses of initial treatments. Further, practitioners who initiate treatment

with behavioral intervention (group parent training and a school DRC) will produce better

outcomes for their patients than those starting with medication. In the face of inadequate

response to initial behavioral treatment, either more intensive behavioral intervention or the

addition of a low dose of medication for school hours will produce improved patient and

family outcomes. In contrast, if medication is the first stage treatment and is insufficient,

adding behavioral treatment is not an effective treatment option–outcomes are worse than

other strategies and parent engagement in subsequent parent training is very poor. Physicians

in particular need to be aware of these facts—simply advising their patients who have started

medication to go to a psychologist for parent training will not result in the desired outcome

—that is, a multimodal intervention. This paper and our companion paper on the costs of

these interventions strongly suggest a reconsideration of the current practice of relying on

medication as first line and typically sole treatment for many ADHD children. These results

suggest that a stepwise approach to treatment, starting with low doses of behavioral

treatment and increasing in intensity or adding medication only if necessary, would be a

cost-effective public health strategy for treatment of childhood ADHD in school and

community settings.

Acknowledgments

Funding

This research was funded by a grant from the Institute of Education Sciences (R324B060045). Dr. Pelham was also
supported in part by grants from the National Institute of Mental Health (MH069614, MH069434, MH092466,
MH53554, MH065899, MH62988), the Institute of Education Sciences (R324J060024, LO30000665A), the

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National Institute of Alcohol Abuse and Alcoholism (AA11873), and the National Institute on Drug Abuse
(DA12414, DA12986).

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practice. Statistics in Medicine. 2011; 30(4):377–399. [PubMed: 21225900]

Wisniewski SR, Fava M, Trivedi MH, Thase ME, Warden D, Niederehe G, … John Rush A MD.
Acceptability of second-step treatments to depressed outpatients: a STAR* D report. The
American Journal of Psychiatry. 2007; 164(5):753–760. [PubMed: 17475734]

Pelham et al. Page 25

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Figure 1.
Participant Flow

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Figure 2.
Study Design

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Figure 3.
Means on Observed Classroom Rules Violations and Out-of-Class Disciplinary Events as a

Function of Treatment Decisions

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Figure 4.
Parent Training Attendance by Initial Treatment Assignment

Note. Figures for the Medication First families consider only those that were rerandomized

to behavioral treatment (M-then-B, N=35).

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Table 1

Sample Characteristics

Variable Medication First Behavioral First

Number of participants 74 72

Child Age in Years 8.3 (2.0) 8.5 (1.8)

Child Gender (% Male) 77% 75%

Child Race

White 76% 84%

Black/African American 17% 7%

Other 7% 8%

Child IQ 99.9 (16.2) 99.2 (12.5)

Other Diagnoses

Oppositional/Defiant Disorder 60% 54%

Conduct Disorder 17% 14%

ADHD Symptoms Endorsed

Inattention 7.6 (1.9) 8.1 (1.5)

Hyperactivity/Impulsivity 7.1 (2.2) 6.8 (2.1)

Parent Disruptive Behavior Disorders Rating

ADHD 1.89 (0.61) 1.99 (0.50)

ODD 1.32 (0.67) 1.29 (0.57)

CD 0.26 (0.28) 0.21 (0.20)

Teacher Disruptive Behavior Disorder Rating

ADHD 1.84 (0.62) 1.78 (0.60)

ODD 1.17 (0.84) 0.95 (0.73)

CD 0.45 (0.59) 0.31 (0.44)

Parental Marital Status (% Single Parent) 11% 7%

Highest Parental Education Level

High School Diploma or Less 10% 10%

Partial college or technical training 17% 14%

2-year degree 25% 19%

4-year degree 24% 31%

Graduate training 25% 26%

Previous Medication Treatment 27% 31%

Note. Groups did not differ significantly on any demographic measure.

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Table 2

Intervention Components

Modality Initial Treatment Secondary/Adaptive Treatment

Medication • 8-hour stimulant equivalent to 0.15
mg/kg methylphenidate b.i.d.

• Increased school dose

• Added evening/weekend doses

Behavioral Treatment

• 8 weekly sessions of group behavioral
parent training (concurrent group
social skills training for children)

• Monthly booster parent training
sessions

• 3 consultation meetings with primary
teacher to establish a school-home
daily report card

• One individual parent training session
to establish home rewards for daily
report card

• Group or individual classroom
contingency management systems
(Barrish, Sauders, & Wolf, 1969)

• Time-out in school

• Tutoring

• Organizational skills training (Schultz &
Evans, 2015)

• School-based rewards

• Weekly Saturday social skills sessions
(Pelham et al., 2008)

• Homework skills training (Power et al.,
2001)

• Paraprofessional-implemented school
rewards programs

• Home-based daily report card

Note. The adaptive components listed represent those offered or recommended as-needed based on individual areas of impairment. Not every child
received every component of the adaptive treatment.

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Table 3

Outcomes at Endpoint by Initial Treatment Assignment

Outcome Medication First Behavioral First Effect Size

Classroom rules violations per hour** 12.6 [10.5, 15.3] 8.4 [6.8, 10.3] IRR = 0.66

Out-of-class disciplinary events per school year† 3.1 [1.8, 5.2] 1.6 [0.9, 2.7] IRR = 0.52

Teacher DBD—ADHD 0.98 (.67) 1.00 (.64) d = −0.02

Teacher DBD—ODD 0.59 (.66) 0.45 (.51) d = 0.24

Teacher SSRS Social Skills Total Score 33.9 (9.5) 36.0 (10.5) d = 0.21

Parent DBD—ADHD 1.44 (.64) 1.45 (.62) d = −0.01

Parent DBD—ODD 1.09 (.71) 0.98 (.65) d = 0.16

Parent SSRS Social Skills Total Score 45.2 (10.8) 45.3 (10.7) d = 0.01

Note. IRR=incidence rate ratio, DBD=Disruptive Behavior Disorders Rating Scale (scores are average scale scores, range 0–3), ADHD=attention
deficit hyperactivity disorder, ODD=Oppositional defiant disorder, SSRS=Social Skills Rating Scale. Values are means with standard deviations in
parentheses (for continuous outcomes) or asymmetric 95% confidence intervals about the mean (for count outcomes). The IRR is the ratio of the
event (e.g., rule violation) incidence rate in one group (here, Behavioral First) to the incidence rate in another group (here, Medication First). The
other effect sizes are Cohen’s D with pooled standard deviation (equations 2.5.1 and 2.5.2, pp. 66–67, Cohen, 1988), and are listed such that a
positive d reflects an advantage of Behavioral First.

†
p<0.10,

**
p<0.01.

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Table 4

Outcomes at Endpoint by Treatment Protocol Followed

Outcome BB protocol BM protocol MB protocol MM protocol

Classroom rules violations per hour 7.2† [5.8, 9.0] 9.3a† [7.6, 11.4] 14.3b [11.1, 18.5] 12.7ab [9.0, 18.0]

Out-of-class disciplinary events per school year 2.6ab [1.1, 6.1] 0.9c [0.5, 1.7] 5.5a† [2.4, 12.9] 1.9bc† [0.9, 4.2]

Teacher DBD— ADHD 1.09 (.65)a 0.91 (.61)a 1.02 (.71)a 0.94 (.63)a

Teacher DBD— ODD 0.48 (.55)ab 0.42 (.46)a† 0.69 (.79)b† 0.50 (.50)ab

Teacher SSRS Social Skills Total Score 35.0 (10.8)ab 36.8 (10.0)a† 33.2 (10.7)b† 34.5 (8.2)ab

Parent DBD— ADHD 1.52 (.63)a 1.37 (.59)a 1.54 (.65)a 1.34 (.60)a

Parent DBD—ODD 1.10 (.69)ab† 0.86 (.58)c† 1.23 (.74)a‡ 0.95 (.64)bc‡

Parent SSRS Social Skills Total Score 44.2 (10.0)a 46.4 (11.2)a 45.0 (10.1)a 45.4 (11.3)a

Note. DBD=Disruptive Behavior Disorders Rating Scale (scores are average scale scores, range 0–3), ADHD=attention deficit hyperactivity
disorder, ODD=Oppositional defiant disorder, SSRS=Social Skills Rating Scale. The first letter of each protocol indicates its first-stage treatment
and the second letter indicates its second-stage treatment, to be implemented in the event of insufficient response (‘B’ for behavioral, ‘M’ for
medication). Values are means with standard deviations in parentheses (for continuous outcomes) or asymmetric 95% confidence intervals about
the mean (for count outcomes). They were calculated using the weighting method as described in Nahum-Shani et al. (2012). Within each row,
means that have no superscript in common are significantly different from each other, p<.05. Cross or doublecross next to a pair of means indicates difference was only marginal, p<.10.

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Pelham et al. Page 34

Table 5

Outcomes at Endpoint by Secondary/adaptive Treatment Given Insufficient Response to Initial Behavioral

Treatment

Outcome B-then-B B-then-M Effect Size

Classroom rule violations per hour* 6.6 [5.1, 8.6] 9.4 [7.5, 11.7] IRR = 1.41

Out-of-class disciplinary events per school year† 3.2 [1.2, 8.3] 1.0 [0.4, 2.7] IRR = 0.30

Teacher DBD—ADHD 1.28 (.65) 1.00 (.65) d = 0.44

Teacher DBD—ODD 0.63 (.60) 0.52 (.49) d = 0.19

Teacher SSRS Social Skills Total Score 32.0 (9.6) 35.0 (9.1) d = 0.31

Parent DBD—ADHD 1.60 (.66) 1.43 (.63) d = 0.26

Parent DBD—ODD† 1.20 (.69) 0.90 (.59) d = 0.45

Parent SSRS Social Skills Total Score 41.8 (9.1) 44.4 (11.2) d = 0.26

Note. B-then-B=began with behavioral treatment and then received higher dose behavioral treatment, B-then-M=began with behavioral treatment
then added medication treatment, IRR=incidence rate ratio, DBD=Disruptive Behavior Disorders Rating Scale (scores are average scale scores,
range 0–3), ADHD=attention deficit hyperactivity disorder, ODD=Oppositional defiant disorder, SSRS=Social Skills Rating Scale. Values are
means with standard deviations in parentheses (for continuous outcomes) or asymmetric 95% confidence intervals about the mean (for count
outcomes). The IRR is the ratio of the event (e.g., rule violation) incidence rate in one group (here, B-then-M) to the incidence rate in another group
(here, B-then-B). The other effect sizes are Cohen’s D with pooled standard deviation (equations 2.5.1 and 2.5.2, pp. 66–67, Cohen, 1988), and are
listed such that a positive d reflects an advantage of B-then-M.

†
p<0.10,

*
p<0.05.

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Table 6

Outcomes at Endpoint by Secondary/adaptive Treatment Given Insufficient Response to Initial Medication

Treatment

Outcome M-then-M M-then-B Effect Size

Classroom rule violations per hour 14.5 [9.5, 22.1] 17.1 [10.9, 26.9] IRR = 1.18

Out-of-class disciplinary events per school year† 2.2 [0.8, 6.6] 8.2 [3.5, 19.6] IRR = 3.66

Teacher DBD—ADHD 1.21 (.63) 1.43 (.71) d = −0.34

Teacher DBD—ODD† 0.70 (.52) 1.15 (.91) d = −0.61

Teacher SSRS Social Skills Total Score 32.2 (6.2) 28.8 (11.0) d = −0.39

Parent DBD—ADHD 1.38 (.60) 1.62 (.63) d = −0.38

Parent DBD—ODD† 1.02 (.65) 1.33 (.73) d = −0.46

Parent SSRS Social Skill Total Score 44.5 (11.2) 44.0 (9.6) d = −0.05

Note. M-then-M=began with medication treatment and then received higher dose medication treatment, M-then-B=began with medication
treatment and then added behavioral treatment, IRR=incidence rate ratio, DBD=Disruptive Behavior Disorders Rating Scale (scores are average
scale scores, range 0–3), ADHD=attention deficit hyperactivity disorder, ODD=Oppositional defiant disorder, SSRS=Social Skills Rating Scale.
Values are means with standard deviations in parentheses (for continuous outcomes) or asymmetric 95% confidence intervals about the mean (for
count outcomes). The IRR is the ratio of the event (e.g., rule violation) incidence rate in one group (here, M-then-B) to the incidence rate in another
group (here, M-then-M). The other effect sizes are Cohen’s D with pooled standard deviation (equations 2.5.1 and 2.5.2, pp. 66–67, Cohen, 1988),
and are listed such that a positive d reflects an advantage of M-then-B.

†
p<0.10.

J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.

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