Examining differential effects of psychological treatment of depressive disorder: An application of trajectory analyses
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Journal of Affective Disord
Research report
Examining differential effects of psychological treatment of
depressive disorder: An application of trajectory analyses
Pim Cuijpers a,b,*, Pol A.C. van Lier c,d, Annemieke van Straten a,b, Marianne Donker e
a Department of Clinical Psychology, Vrije Universiteit Amsterdam, The Netherlandsb Trimbos Institute, Utrecht, The Netherlands
c Department of Developmental Psychology, Vrije Universiteit Amsterdam, The Netherlandsd Department of Child and Adolescent Psychiatry, Erasmus MC/Sophia Children’s Hospital, Rotterdam, The Netherlands
e Institute of Health Policy and Management, Erasmus MC, Rotterdam, The Netherlands
Received 23 March 2005; received in revised form 31 August 2005; accepted 5 September 2005
Abstract
Background: Although different psychological treatments of depression seem equally effective, studies in this area have not
taken sufficient account of the heterogeneity among patients. Modern techniques for longitudinal data analysis can be helpful in
examining differential effects of psychological interventions on specific subpopulations of patients.
Methods: Outpatients in mental health care, diagnosed with DSM-IV major depressive disorder, were randomly assigned to
cognitive behavior therapy (N =199) or treatment as usual (N =226). Every 3 months for a total of 1.5 years, depressive
symptomatology was measured using the SCL-90. Growth mixture modeling techniques were used to identify different
trajectory classes of patients. The impact of type of treatment (treatment as usual vs. cognitive behavior therapy) was examined
for each identified trajectory.
Results: On average, patients in both test conditions improved significantly from baseline to posttest, and no significant
difference was found between the conditions. However, four different trajectory classes could be distinguished within the
sample. Most patients were classified into the two classes with the lowest depression scores at baseline (31% and 33% of the
total sample). For these two classes, no significant differences in the course of depressive symptoms were found between the
two conditions. In the two classes with the more severe depression scores (10% and 26% of the sample), however, cognitive
behavior therapy was significantly more effective than treatment as usual.
Conclusions: Although different treatments may seem to be equally effective, this does not have to be true for all classes of
patients. Longitudinal research on the treatment of mental disorders should take heterogeneity among patients into account.
D 2005 Elsevier B.V. All rights reserved.
Keywords: Depressive disorders; Randomized controlled trial; Cognitive behavior therapy; Growth mixture modeling
0165-0327/$ - s
doi:10.1016/j.jad
* Correspondi
The Netherlands
E-mail addre
ers 89 (2005) 137–146
ee front matter D 2005 Elsevier B.V. All rights reserved.
.2005.09.001
ng author. Department of Clinical Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam,
. Tel.: +31 20 598 8757; fax: +31 20 598 8758.
ss: P.Cuijpers@psy.vu.nl (P. Cuijpers).
P. Cuijpers et al. / Journal of Affective Disorders 89 (2005) 137–146138
1. Introduction
Dozens of well-designed randomized trials have
shown the efficacy of brief psychological interven-
tions for major depression, such as cognitive behavior
therapy (Churchill et al., 2001; Gaffan et al., 1995;
Scott, 2001), interpersonal psychotherapy (Reynolds
et al., 1999), and problem solving therapy (Barrett et
al., 2001; Dowrick et al., 2000; Williams et al., 2000).
Whether all psychological interventions are equally
effective or some are superior to others, however, is
the subject of continuing debate (Beutler, 2002;
Luborsky et al., 1975, 2002; Shadish and Sweeney,
1991). Currently, most meta-analytic evidence indi-
cates that different types of brief psychological
interventions result in comparable effects on depres-
sion (Luborsky et al., 2002). This lack of differential
effects is usually explained by nonspecific elements
which are common to all interventions, such as an
intensive relationship between the patient and the
therapist, the expectation of the patient to be cured,
the dritualT of the therapy, and the presentation of a
clear drationaleT with which the problems of the
patient can be explained (Duncan, 2002).
Critics of this view, however, have pointed at the
complexity of determining the presence of specific
effects. They have argued that significant differences
between psychotherapies do exist but that the number
of potential effect predictors is very large and that
most studies do not have sufficient statistical power to
detect them (Beutler, 2002; Kazdin and Bass, 1989).
Furthermore, the mean effects of various psycholog-
ical treatments do not necessarily imply that there are
no identifiable differences in effects on individuals or
subpopulations within a sample. The research on
mediators and moderators of outcome increasingly
shows that some subpopulations do benefit less than
others do from psychological interventions (Shadish
and Sweeney, 1991). For example, there is growing
evidence that comorbid anxiety disorder (Albus and
Scheibe, 1993; Brown et al., 1996) and comorbid
personality disorder (Reich, 2003) both reduce the
effects of psychological treatment of depression.
A fundamental problem inherent in the research
studies on moderators of outcome is that these studies
are looking for subpopulations who benefit more or
benefit less from an intervention, but actually examine
only characteristics which may be indicative of these
subpopulations. Recently, methods have become
available to identify distinct groups of individuals,
differing in the initial level and course of a specific
behavior, through the empirical identification of
developmental trajectories (Muthen and Shedden,
1999; Nagin, 1999). The advantage of these methods
is that the identification of subpopulations within a
sample is based on the target behavior itself (in our
case depressive symptoms over time). These techni-
ques also make it possible to examine whether the
effects of interventions differ for different categories
of patients, to ascertain which characteristics predict
membership of one of these categories, and to
establish whether outcomes are different for each
category (Muthen, 2001; Muthen et al., 2002).
In the current study, data from a large pragmatic
randomized trial comparing cognitive behavior ther-
apy and treatment as usual were used to demonstrate
the possibilities of these analytical techniques in
examining the relative effects of interventions on
specific subpopulations of patients. We focus on the
following questions: (1) what is the impact of
cognitive behavior therapy and treatment as usual on
the development of depressive symptoms; (2) how
many trajectory classes of depressive symptoms can
be distinguished; (3) do treatment as usual and
cognitive behavior therapy have a different effect on
patients in different trajectory classes; and (4) how can
patients who follow each of the trajectory classes be
identified?
2. Method
2.1. Procedure and respondents
We used the data of a pragmatic randomized trial
designed to examine whether the effects of psycho-
logical treatments for patients with either mood or
anxiety disorders, which have been typically studied
in research settings, can also be generalized to routine
practice settings. The methods of this study have been
described in detail elsewhere (Van Straten et al.,
submitted for publication). In brief, subjects were
recruited at 7 outpatient mental health centers
(MHCs) at 12 different locations (both rural and
urban regions) in the Netherlands. Patients were
enrolled in two steps (Fig. 1). First, all patients
Fig. 1. Patients included in the analyses.
P. Cuijpers et al. / Journal of Affective Disorders 89 (2005) 137–146 139
between 18 and 65 years old who were in need of
mental health care were screened during the inclusion
period (February 2000 – October 2001) on exclusion
criteria: psychotic or manic symptoms, cognitive
impairments such as dementia or mental retardation,
illegal hard drug dependence (patients with alcohol
were not excluded), high suicide risk, or poor
command of the Dutch language. The remaining
patients were then screened for the presence of mood
and/or anxiety disorders. Second, all remaining
patients were interviewed at home (baseline assess-
ment) by a trained research assistant to determine the
presence of mood and/or anxiety disorders with the
Composite International Diagnostics Interview
(CIDI). The CIDI, a structured interview developed
by the World Health Organization (World Health
Organization, 1994; Ter Smitten et al., 1998), enables
trained lay interviewers to assess psychiatric diagno-
ses according to the DSM-IV. Patients with the
following DSM-IV diagnosis were included: major
depressive disorder (single episode or recurrent),
dysthymic disorder, panic disorder (with or without
agoraphobia), social phobia, or generalised anxiety
disorder, including comorbid diagnoses. All eligible
patients were asked to participate in the study. After
full explanation of the study, 702 patients gave written
informed consent (Fig. 1). These 702 patients were
randomized. We used block randomization, stratified
by MHC setting. The randomization scheme was
derived by computer and managed centrally at the
research centre. When a patient was included, the
researchers opened an envelope and the randomization
outcome was reported to the MHC. Patients and
therapists were informed about the randomization
outcomes, but the research assistants who performed
the interviews were kept blind during the whole study.
Because of the complexity of the analyses and the
illustrative purposes of this study, only the subjects
randomized to CBT and TAU, who had a diagnosis of
major depressive (MDD) are included in this study
(TAU, n =226, CBT, n =199, Fig. 2).
Patients were interviewed at baseline and then
every 3 months until 7 measurements were taken
(1.5 years after baseline). The presence of DSM-IV
mental disorders was established using the CIDI
during face-to-face interviews at baseline and 1.5 to
2 years after baseline. Other interviews were
conducted by telephone.
P. Cuijpers et al. / Journal of Affective Disorders 89 (2005) 137–146140
Baseline characteristics of the included subjects
have been described in detail elsewhere (Van Straten
et al., submitted for publication). In brief, 61% of the
patients were female, their mean age was 36 years
(standard deviation (S.D.)=10), 66% had a paid job,
and 35% used anti-depressive medication. The per-
centage of subjects who used anti-depressive medica-
tion at baseline was similar in the TAU and CBT
condition (v2 (1, N =393)= 0.865, p N0.05). At
baseline, 40% (TAU) and 41% (CBT) had a severe
MDD and 46% (TAU) and 47% (CBT) had an anxiety
disorder.
2.2. Interventions
Cognitive Behavior Therapy is characterized by a
structured approach and is primarily aimed at restruc-
turing irrational cognitive assumptions about oneself
which are responsible for negative interpretations of
situations, negative emotions, and finally to self-
undermining actions. CBT has proven its efficacy for
depressive and several types of anxiety disorders
(Churchill et al., 2001; Gaffan et al., 1995; Scott,
2001; Van Balkom et al., 1997), although its effective-
ness in real life conditions has not beenwell established.
Treatment as usual is the treatment as delivered in
current daily practice in the MHCs in the Netherlands.
In TAU, a multidisciplinary team chooses a therapeu-
tic approach which is tailored to the individual’s
needs, taking into account the specific constellation of
problems and characteristics of each different patient.
As a result, any kind of therapy may be chosen from a
wide variety of approaches.
Each therapist, in any of the participating centers,
was expected to apply only one of the treatments.
Therapists had to be experienced in the treatment
provided in this trial. A limited protocol was
developed for CBT by experts in cooperation with
the participating therapists. The therapists received a
2-day training in using the protocol. Since there are
long waiting lists in the Netherlands, we allowed a
waiting time before the start of the treatment with a
maximum of 4 months for both TAU and CBT.
Furthermore, severely ill patients were allowed to
receive antidepressant medication in addition to the
treatment to which they were randomized. Prescrip-
tion was protocolized in accordance with current
guidelines.
The mean number of days after randomization at
which the treatment started was 75 days (S.D.=61).
The mean number of sessions received was 10
(S.D.=11). The mean treatment duration was 224
days (S.D.=202), with CBT (223 days; S.D.=180)
being significantly shorter than TAU (265 days;
S.D.=232). A considerable proportion of the subjects
(16%) did not receive the treatment they were
randomized to, but this was not significantly different
in CBT and TAU.
2.3. Instruments
Depressive symptomatology was assessed with
the Depression Scale of the Symptom Check List-
90 (SCL-90; Ettema and Arrindell, 2003). This
scale measures the severity of symptoms and
consists of 16 psychological symptoms which must
be rated on a 5-point scale, ranging from 1 (not
distressed by the symptom) to 5 (extremely
distressed by the symptom). A total score can be
obtained by adding the item scores, ranging from
16 (no depressive symptomatology) to 80 (high
level of depressive symptoms). For this study, we
recalculated the SCL-D scores so that 0 was the
lowest score.
Mood and anxiety disorders were assessed at
baseline and 18 to 24 months after baseline, using
the CIDI-Auto (World Health Organization, 1994)
Dutch version (Ter Smitten et al., 1998). The CIDI is a
standardized diagnostic interview for the assessment
of mental disorders, developed by the World Health
Organization. It was designed for use by trained
interviewers who are not clinicians. Its reliability has
been demonstrated to be good to excellent, and the
validity has been demonstrated to be adequate
(Andrews and Peters, 1998; Wittchen, 1994). With
the CIDI, the severity of major depressive disorder
can be assessed (mild, moderate, severe), and a
distinction is made between first or recurrent major
depressive disorder.
2.4. Attrition
The response of the 702 patients initially included
in the study was 65% (n =459) after 1 year and 69%
(n =484) for the last diagnostic interview (at 1.5 to 2
years after baseline).
P. Cuijpers et al. / Journal of Affective Disorders 89 (2005) 137–146 141
Over the 7 repeated assessments, the majority of
subjects (75%) had missing values on one or more
SCL depression reports but 76% of the sample
completed at least four assessments. One hundred
and thirty-seven subjects (32%) did not participate in
the final clinical interview. Missing one or more self-
reported SCL assessments was not related (at p b0.05
level) to presence of MDD, severe MDD, dysthymic
disorder or anxiety disorder, nor to SCL depression
scores at pre-treatment. Likewise, missing at the final
clinical interview was not related ( p b0.05) to the
presence of MDD, severe MDD, dysthymic disorder
or anxiety disorder, nor to SCL depression scores at
baseline.
2.5. Statistical analyses
We started by analyzing the course of depressive
symptomatology according to the SCL-depression
scale over the follow-up period by means of trajectory
analyses. Trajectory analyses describe the course of
depressive symptomatology through a regression
function, using continuous latent growth factors
(intercept, slope, and quadratic slope). The intercept
represents the level of depressive symptoms at pre-
treatment. The change in depressive symptoms over
time is accounted for by one or more growth factors
i.e. linear slope or quadratic slope.
In the second stage, growth mixture modeling
(GMM) was used to determine the number of
developmental trajectories of depressive symptom-
atology for subjects who received TAU and CBT
(Muthen and Shedden, 1999; Nagin, 1999). The
objective of GMM was to find the smallest number
of classes of subjects with similar courses of
depressive symptoms. GMM estimates mean growth
curves, based on the initial status (intercept) and
change (linear slope/quadratic slope), for each class of
subjects and captures individual variation around
these growth curves by the estimation of factor
variances for each class. Separate GMM models were
analyzed for TAU and CBT.
Different considerations may be used in deciding
on the optimal number of classes (Muthen and
Muthen, 1998–2004). The first is the Bayesian
information criterion (BIC; Kass and Raftery, 1993;
Schwartz, 1978) in which lower BIC values indicate
improvement of the model when compared to the
model with one class less. Another consideration is
the usefulness of the classes, which is the subjective
comparison of the developmental trajectories, the
number of subjects in each class, and the differences
in outcomes between classes. In this study, both
criteria were used.
In the third and final stage, general growth mixture
modeling (GGMM; Muthen and Muthen, 2000) was
used to compare the developmental trajectories of
subjects who received TAU with subjects who
received CBT. In GGMM, the effect of type of
treatment (0 =TAU, 1=CBT) on the course of
depressive symptoms can be estimated by regressing
the continuous latent variables on treatment status in
each of the classes (Muthen et al., 2002). Factors
predicting class membership (presence of a severe
depression, and a comorbid dysthymic disorder or
anxiety disorder) were added to the model. The
estimated parameters of this GGMM were: (1) latent
class membership probabilities, which gives the
probability for each individual to belong to each of
the classes; (2) the means and variances of the
continuous latent growth factors of the depressive
symptoms for each of the classes; (3) estimates of the
regression coefficient of type of treatment on the
continuous latent growth factors for each of the
classes; and (4) the regression coefficients (odds
ratios), predicting class membership for each patient,
by each of the predictor variables.
Trajectory analyses were conducted with Mplus
3.0 (Muthen and Muthen, 1998–2004). Since missing
data on the SCL-depression scores were random, the
missing data module was used to optimally use the
available data.
3. Results
3.1. Course of depressive symptoms
Fig. 2 presents the 1.5-year course of SCL
depression of subjects who received TAU, or CBT.
Regardless of type of treatment, subjects had on
average a similar course of depressive symptoms, with
a significant improvement in SCL depression scores
over time. The percentage of subjects with a DSM-IV
diagnosis of MDD (mild, moderate, or severe),
subjects with a severe MDD, and subjects with an
0
5
10
15
20
25
30
35
0 3 6 9 12 15 18
SC
L D
epre
ssio
n
months
Cognitive Behavior Therapy
Treatment as Usual
Fig. 2. Developmental course of SCL-depression for subjects receiving treatment as usual or cognitive behavior therapy.
P. Cuijpers et al. / Journal of Affective Disorders 89 (2005) 137–146142
anxiety disorder at post-test (1.5–2 years after
baseline) are presented in Table 1. The large decreases
in the percentages of subjects with any of these
psychiatric conditions at post-test substantiated the
decreases in SCL depression scores. At post-test,
approximately 24% (28–20%) of the subjects had an
MDD. 11% of the subjects who received TAU had a
severe MDD at post-test compared to 5% of the
subjects who received CBT. This difference was
marginally significant (v2 (1, N = 425) = 3.4,
p b0.06). 28% of subjects who received TAU and
19% of subjects who received CBT had an anxiety
disorder at post-test. This was a trend towards
significance (v2 (1, N =425)=2.9, p b0.10), suggest-
ing a (small) positive effect of CBT over TAU.
Table 1
Post-test comparison of prevalence (%) of severe major depressive disord
received treatment as usual or cognitive behavior therapy for the total sam
DSM-IV condition at the end of the study
All patients with MDD Patien
TAU CBT Test TAU
Total sample 28 20 ns 11
Trajectory classes
Class 1 100 64 * 69
Class 2 49 14 * 14
Class 3 22 23 ns 6
Class 4 0 7 ns 0
MDD=major depressive disorder, TAU=treatment as usual, CBT=cogniti
significant.
3.2. Trajectory classes of depression
The number of trajectory classes was identified for
TAU and CBT separately. Two (BIC: TAU=8491,
CBT=7370), three (TAU=8409, CBT=7292), four
(TAU=8371, CBT=7261) and five (TAU=8353,
CBT=7265) trajectory classes were fitted. Fitting
more trajectory classes resulted in non-converging
solutions. Allowing the indicator variance and inter-
cept variance for the last trajectory class (low
depressive symptoms) to be different from the other
trajectory classes improved the fit of the four-class
model (BIC: TAU=8233, CBT=7125). Allowing for
variances to be different between trajectory classes did
not result in a better fit in the five-class model,
er, major depressive disorder, and anxiety disorder for subjects who
ple and for trajectory classes 1 to 4
ts with a severe MDD Patients with an anxiety disorder
(with or without MDD)
CBT Test TAU CBT Test
5 ** 28 19 **
36 ** 100 50 *
0 * 46 31 ns
2 ns 20 13 ns
2 ns 4 9 ns
ve behavior therapy. v2 tests (df =1); *p b0.05; **p b0.10; ns=not
P. Cuijpers et al. / Journal of Affective Disorders 89 (2005) 137–146 143
compared to the four-class model. It was therefore
decided that the four-class model best fitted the data
for both TAU and CBT. The percentage of subjects
who had missing SCL depression scores (v2 (3,
N =425)=0.9, p N0.05) or who did not participate in
the final clinical interview (v2 (3, N =425)=4.9,
p N0.05) was similar for each of the four trajectory
classes.
In the final model, all subjects were analyzed
simultaneously in the GGMM analyses. Predictor
variables were included if they predicted class-
membership at p b0.05. Using this criterion, three of
the four examined predictors (presence of a severe
MDD, a comorbid dysthymic disorder, and a comor-
bid anxiety disorder) were included in the model,
whereas the fourth examined predictor (first depres-
sion vs. recurrent depression) was not. To account for
the effect of type of treatment on the course of
depressive symptoms in each of the trajectory classes,
the continuous latent variables were regressed on
treatment status. No effect of type of treatment on the
intercepts was found for subjects who were classified
to each of the four classes. This indicates that given a
trajectory class, no differences in SCL depression
scores at baseline were found between TAU and CBT.
To test wheter the results of the final model were
consistent among the samples, two random sub-
samples were drawn and the model was reran. First,
parameters were freely estimated for each of these
sub-samples. We then fixed the parameters at the
0
10
20
30
40
50
0 3 6 9
SC
L D
epre
ssio
n
mon
Fig. 3. Developmental trajectories of SCL-depression for subjects
estimates found in the model on the total sample. For
each sub-sample, it was found that the parameter
estimates were similar to those found in the total
sample (sub-sample 1: v2 (15, N =221)=7.1, p b0.05;
sub-sample 2: v2 (15, N =204)=8.9, p b0.05) indi-
cating that the found results were consistent within
this sample.
3.3. Trajectory classes of depression and type of
treatment
Fig. 3 presents the four resulting trajectory classes.
Ten percent of all subjects were allocated to class 1.
These subjects had the highest SCL depression scores
at baseline. The level of depressive symptoms
remained consistently high in subjects who received
TAU. In contrast, the significant estimate of the type
of treatment on the development of SCL depression
(est.=�1.5, S.E.=0.4, p b0.01) indicated that sub-
jects who received CBT had a significant improve-
ment in their depressive symptoms over the 1.5-year
follow-up period. To assess the clinical relevance of
the mean difference at post-test (after 1.5 years),
Cohen’s d was calculated, indicating the standardized
difference between TAU and CBT at post-test. The
standardized difference was found to be 0.75,
indicating a large effect (Cohen, 1988).
The percentage of subjects who had (severe) MDD
or comorbid anxiety disorder at post-test for each of
the classes is presented in Table 1. The reduced risk of
12 15 18
Class 3
Class 4
ES = .75
ES = .86
Class 1
Class 2
Class 3
Class 4
ths
Treatment as Usual
Cognitive Behavior Therapy
receiving treatment as usual or cognitive behavior therapy.
P. Cuijpers et al. / Journal of Affective Disorders 89 (2005) 137–146144
having these psychiatric conditions for class 1
subjects who had been treated with CBT, compared
to class 1 subjects who had been treated with TAU,
substantiates the positive effect of CBT over TAU,
which was found in the GGMM analyses on the SCL-
scale. For instance, all class 1 subjects (100%) who
received TAU had an MDD and an anxiety disorder at
post-test, compared to 64% (MDD) and 50% (anxiety)
for class 1 subjects who received CBT.
Twenty-six percent of the subjects were allocated
to class 2. These subjects had, on average, the second
highest SCL depression scores of all subjects at
baseline. All of these subjects improved on their
depressive symptoms. The significant estimate of type
of treatment on the slope of class 2 depressive
symptoms indicated, however, that subjects who
received CBT had significantly larger decreases in
depressive symptomatology than subjects who re-
ceived TAU (est.=�1.3, SE=0.4, p b0.01). The size
of the standardized mean difference of SCL depres-
sive symptoms at post-test is 0.86, which is a large
effect (Cohen, 1988). 14% of the subjects receiving
CBT, compared to 49% of subjects who received TAU
still had an MDD at post-test (Table 1). Likewise,
none of the subjects who received CBT still had a
severe MDD, compared to 14% of those who received
TAU.
The remaining subjects were allocated to class 3
(31%) and class 4 (33%). The differences between
these two trajectory classes are the SCL depression
scores at baseline (S.D. class 3=9.3, S.D. class
4=11.2). The depressive symptoms improved rapidly
to low level (class 3) or almost absence (class 4) of
SCL depressive symptoms after approximately 9–12
months, regardless of type of treatment (Fig. 3). The
percentage of subjects who had a (severe) MDD and/
or anxiety disorder at post-test (Table 1) is similar for
subjects in both treatments, which indicates that CBT
Table 2
Association between trajectory class-allocation and presence of severe de
Comobrid Class 1 vs. Class 4
OR 95% CI
Severe depression 4.6 (1.8–11.8)*
Dysthymic disorder 2.9 (1.1–8.0)*
Anxiety disorder 5.9 (2.3–15.1)*
All participants met criteria for DSM-IV major depressive disorder. All asso
* p b0.05.
and TAU are equally effective for adults in class 3 and
class 4.
3.4. Predicting trajectory classes
Table 2 presents the increase in risk, expressed as
odds ratios, of being allocated to class 1, class 2 or
class 3, compared to class 4 (subjects who completely
recovered) as a function of the presence of a severe
MDD, dysthymic disorder and/or a comorbid anxiety
disorder at baseline. Being classified as class 1 was
predicted by the presence of a severe MDD, the
presence of dysthymic disorder and the presence of an
anxiety disorder at pre-treatment. Class 2 subjects
were predicted by the presence of a severe MDD and
a comorbid anxiety disorder, compared to class 4
subjects. The difference between class 3 and class 4 is
the presence of a severe MDD at baseline.
4. Discussion
We wanted to illustrate how trajectory analyses
techniques can help in identifying subpopulations
with depressive disorders, based on their initial level
of depressive symptomatology and the course of
symptoms over time. We included predictor variables
to identify the subjects in each of the trajectory
classes. We additionally wanted to study if TAU and
CBT had a different impact on the courses of
depressive symptomatology in the different trajectory
classes. We showed that the overall mean course of
depressive symptomatology for subjects treated with
CBT was comparable to the course of symptomatol-
ogy for subjects who received TAU. However, for the
empirically identified subpopulations, this was not
true. CBT proved to be superior to TAU for subjects
with higher initial levels of depressive symptoms.
pression, dysthymic disorder and anxiety disorder at baseline
Class 2 vs. Class 4 Class 3 vs. Class 4
OR 95% CI OR 95% CI
2.7 (1.4–5.4)* 2.6 (1.2–5.5)*
1.6 (0.8–3.2) 1.1 (0.5–2.6)
3.2 (1.5–6.9)* 1.4 (0.6–3.4)
ciations are given as multiple odds ratios (95% confidence interval).
P. Cuijpers et al. / Journal of Affective Disorders 89 (2005) 137–146 145
These findings are important for several reasons.
First, we demonstrated that several subpopulations
within a population of subjects, diagnosed with MDD,
can indeed be distinguished. This means that not all
patients with MDD follow an identical course of
depressive symptomatology. Further research is nec-
essary to examine whether the four trajectory classes
we found are typical for outpatients or are also found
in other patient groups, for example in primary care.
Comparable trajectories may possibly be found for
patients who do not receive treatment. It is also
important to examine whether these trajectory classes
can be distinguished in subjects with only major
depression, without comorbid anxiety or mood
disorders. If these categories can indeed be validated,
this will allow the development of specific treatments
for each of these categories.
The development of specific treatments for differ-
ent subpopulations diagnosed with MDD seems to be
necessary, especially for the patients with the highest
level of initial depressive symptoms. These patients
do benefit more from CBT compared to treatment as
usual, but still have the highest level of depressive
symptoms at post-test, compared to the other classes.
A more intensive treatment seems to be necessary for
these patients. Possibly, the patients in the categories
with less severe symptoms might improve even with a
minimal intervention.
Second, despite the many indications from primary
research and meta-analyses that different psycho-
therapies are equally effective (Luborsky et al., 2002),
this is not necessarily true when different trajectory
classes are distinguished. This earlier research did not
account sufficiently for the heterogeneity among
patients. In our study, a clinically significant superi-
ority of CBT over TAU was demonstrated for those
most in need of treatment. This suggests that to
thoroughly compare the effectiveness of different
psychotherapies, research should give due consider-
ation to the heterogeneity within samples of patients
with MDD.
A third reason why our finding of different
trajectories is important is that this can strengthen
the research on moderators of effects of interven-
tions. We no longer have to limit this research to
examining specific moderators, which can be
compared to searching for a needle in the haystack,
but we can concentrate on empirically defined
trajectory classes. The identification of predictors
of class allocation will allow us to predict much
better who will benefit most from which type of
intervention. On the basis of the current study,
several moderators were identified. The presence of
a severe MDD, a dysthymic disorder or an anxiety
disorder was used to predict which subjects were
allocated to each of the four trajectory classes.
These pre-existing psychiatric conditions uniquely
predicted who would be classified to each of the
trajectory classes. A severe MDD, a dysthymic
disorder, and a comorbid anxiety disorder predicted
which persons were allocated to class 1. The
presence of a severe MDD and an anxiety disorder
predicted who was to be in class 2. These findings
substantiated the finding that heterogeneity is
present within a sample of clinically referred adults.
The finding that comorbid conditions exacerbate the
severity of depressive symptoms is in accordance
with prior findings (Albus and Scheibe, 1993;
Brown et al., 1996). Future studies should examine
whether comparable or additional moderators can be
identified which can help to identify patients in
need of specific treatments.
There are limitations to this study. First, the
number of subjects in the two classes of subjects
with the most severe depressive symptomatology was
relatively small, resulting in a low statistical power.
The fact that we did find statistically significant
differences between the two treatments, however,
indicates that these effects are considerable. Second,
the interventions were conducted in routine practice
and although intervention fidelity was monitored, we
cannot be sure whether the protocols were always
used as planned. Because of these limitations, we
have to be cautious in the interpretation of these
findings. We also have to stress that the methods of
analysing the data we have used in this study are quite
complicated, which may limit the possibilities of
using it in future research.
Despite these limitations, we found clear indica-
tions that there are not only important differences
between patients with depressive disorders but also
that the effects of psychotherapies are different for
different classes of patients. This results can be used
for future classifications of patients which indicate
which treatment is most effective for which type of
patient.
P. Cuijpers et al. / Journal of Affective Disorders 89 (2005) 137–146146
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