Treating a broader range of depressed adolescents with combined therapy

Accepted Manuscript

Treating a broader range of depressed adolescents with combined therapy Simon Foster PhD , Prof. Meichun Mohler-Kuo ScD PII: DOI: Reference:

S0165-0327(18)30384-7 https://doi.org/10.1016/j.jad.2018.08.027 JAD 10024

To appear in:

Journal of Affective Disorders

Received date: Revised date: Accepted date:

23 February 2018 26 July 2018 7 August 2018

Please cite this article as: Simon Foster PhD , Prof. Meichun Mohler-Kuo ScD , Treating a broader range of depressed adolescents with combined therapy, Journal of Affective Disorders (2018), doi: https://doi.org/10.1016/j.jad.2018.08.027

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ACCEPTED MANUSCRIPT Highlights 

Response distributions of depressed adolescents were compared across treatments Cognitive-behavioral therapy was no more effective than placebo

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Fluoxetine was effective only in the middle range of response levels

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Combining cognitive-behavioral therapy and fluoxetine was broadly effective

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Combining the two treatments was also effective in low responders

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ACCEPTED MANUSCRIPT

Treating a broader range of depressed adolescents with combined therapy

Authors:

Meichun Mohler-Kuo 1,2,3, ScD, Prof.

Author affiliations: Epidemiology, Biostatistics and Prevention Institute

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University of Zurich Hirschengraben 84

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8001 Zürich, Switzerland

Swiss Research Institute for Public Health and Addiction associated with the University of

Zurich

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Konradstrasse 32

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Simon Foster 1,2, PhD

La Source, School of nursing sciences

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8031 Zurich, Switzerland

HES-SO University of Applied Sciences and Arts of Western Switzerland Av. Vinet 30

1004 Lausanne, Switzerland

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Corresponding author:

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Simon Foster 1

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Epidemiology, Biostatistics and Prevention Institute

University of Zurich Hirschengraben 84 8001 Zürich, Switzerland Phone: +41 43 556 40 04 Fax:

+41 41 44 448 11 70

Email: [email protected] 3

ACCEPTED MANUSCRIPT Abstract Background: Traditional statistical analyses of clinical trials encompass the central tendency of outcomes and, hence, are restricted to a treatment‘s average effectiveness. Our aim was to get a more complete picture of the effectiveness of standard treatment options for adolescent depression, by analyzing treatment effects across low, middle, and high levels of response.

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Methods: Secondary data analysis was performed of the Treatment for Adolescents with Depression Study (TADS, ClinicalTrials.gov, NCT00006286), a randomized controlled trial comparing fluoxetine (FLX), cognitive-behavioral therapy (CBT), and their combination (COMB) against placebo treating adolescents with major depression (n=439). The

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proportional change from baseline to week 12 in the Children‘s Depression Rating ScaleRevised was used as an index of response. Response levels were analyzed via quantile regression models, thereby estimating treatment effects across the entire response level

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distribution, adjusted for baseline depression, study site, and patients‘ treatment expectancies. Results: Whereas CBT was no more effective than placebo across response levels, COMB

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was more effective than FLX in that its quantile treatment effects were both larger in magnitude and spread out across a broader range of response levels, including the low end of

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1.33-1.45).

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the response level distribution. Cohen‘s d of the difference was 1.39 (95% confidence interval

Limitations: Ad-hoc analysis using data from a trial that was not originally designed to

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accommodate such analysis. Conclusion: The combination of cognitive-behavioral therapy and fluoxetine was more effective than either treatment used alone, not just in average effectiveness, but in the breadth of patients in whom it was effective.

Key words

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ACCEPTED MANUSCRIPT adolescent; major depressive disorder; second-generation antidepressive agents; cognitive

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therapy; combined modality therapy; statistical data interpretation

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ACCEPTED MANUSCRIPT Introduction Among young people, major depression accounts for a substantial portion of the burden of disease (Gore et al., 2011; Merikangas et al., 2010). Furthermore, depression has a variety of adverse effects, both immediately and in adulthood. In particular, depression during adolescence is associated with an increased risk for depression and other psychopathology in

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adulthood (Fergusson and Woodward, 2002; Jones, 2013; Rutter et al., 2006); an increased risk for self-harm and suicidal behaviors (Fergusson and Woodward, 2002; Gould et al., 1998; Hawton et al., 2012; Thapar et al., 2012); an increased risk for reduced social functioning and work difficulties (Costello and Maughan, 2015; Fergusson and Woodward, 2002; Thapar et

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al., 2012); and an increased risk of future adverse birth outcomes among women (NkansahAmankra and Tettey, 2015). It is clear, then, that depression in adolescents needs to be treated

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swiftly and effectively.

The ‗Treatment for Adolescents with Depression Study‘ (TADS) was a corner-stone clinical

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trial that tested the effectiveness of standard treatment options for major depression in adolescents, including treatment with the selective serotonin reuptake inhibitor (SSRI)

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fluoxetine, treatment with cognitive-behavioral therapy, and these two treatments combined

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(March et al., 2004; Thapar et al., 2012). Statistical analysis of the acute phase indicated superiority of fluoxetine used alone, and of the combined treatment over a pill placebo, as

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well as of combined treatment over either fluoxetine or cognitive-behavioral therapy used alone; cognitive-behavioral therapy, however, failed to outperform the placebo (March et al., 2004).

Treatment effects were estimated using standard statistical methods, including mixed-effects and logistic regression (March et al., 2004). Whereas such analyses are indispensable to establishing a treatment‘s general level of effectiveness, they only truly examine outcomes for 6

ACCEPTED MANUSCRIPT each treatment in terms of their central tendency, for example their mean value across all subjects within each treatment arm. However, looking at central tendency fails to reveal whether any detected treatment effect exists only on average or across the entire distribution of outcome values (Cleveland, 1994; Koenker and Hallock, 2001; Wilcox, 2012).

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The quantile regression framework allows for a more complete look at the differences between the outcome distributions of two treatment conditions. With quantile regressions, the quantiles of an outcome‘s distribution are modeled as a function of the predictor variable(s), in contrast to the traditional regression framework, wherein only the mean of the outcome is

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modeled (Beyerlein, 2014; Briollais and Durrieu, 2014; Koenker and Hallock, 2001; Petscher and Logan, 2014). This allows for comparing all quantiles of the treatment condition‘s outcome distribution against the corresponding quantiles of the placebo condition‘s outcome

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distribution. Comparing quantiles is the most informative way of comparing two groups because it shows whether the groups differ in a simple or in a complicated way (Cleveland,

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1994; Wilcox, 2012). In the simple case, one group has consistently higher values than the other. In a more complicated case, low, middle, and high outcome values differ by different

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amounts. It is possible that the groups differ in an even more complicated way, for example

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with one group outperforming the other at lower outcome values, but being outperformed at higher outcome values. Such complicated group differences cannot be uncovered by

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comparing the groups‘ central tendencies.

Assessing a treatment‘s effect across the entire outcome range would be especially attractive if the outcome is an index of response (e.g., indexing the degree of change from baseline to the trial‘s end), since such an analysis would show whether a given treatment has a consistent versus differential effect on low, middle, and high response levels. Thus, rather than examining whether the average response level is higher among subjects receiving active 7

ACCEPTED MANUSCRIPT treatment relative to controls, one could examine whether the treatment has some level of effectiveness across the entire range of response levels, including special subject subsets like low and high responders (Beyerlein, 2014; Briollais and Durrieu, 2014; Petscher and Logan, 2014). A treatment that is able to exert a consistent effect across response levels is more

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clinically valuable than a treatment with a more restricted breadth of effectiveness.

Our current aim was to gain a more complete picture of TADS treatment effects, by re-

evaluating them across the entire response range. We did so by analyzing patients‘ change in depression severity from baseline to the end of the TADS acute phase as an index of response,

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using quantile regression models (Beyerlein, 2014; Briollais and Durrieu, 2014; Koenker and Hallock, 2001; Petscher and Logan, 2014). This approach allowed us to analyze both central tendency and outcome extremes, thereby providing more insights into the breadth of the

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treatments‘ effectiveness than traditional regression analyses. Since we are not aware of any comparable previous analyses, the study was exploratory. The research question was whether

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Methods

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treatment effectiveness varied between low, middle, and high response levels.

Study design

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The current study entailed secondary analysis of TADS trial data (ClinicalTrials.gov, NCT00006286). TADS is a randomized controlled trial that was designed to compare the effectiveness of cognitive-behavioral therapy CBT), a specific SSRI called fluoxetine, and the two treatments combined, all versus a pill placebo for the treatment of adolescents with a major depressive disorder. We analyzed data from the acute phase of the trial, which encapsulated the first 12 weeks of treatment. The data was acquired through a limited access data certificate by the first author from the National Institute of Mental Health (NIMH) Data 8

ACCEPTED MANUSCRIPT Archive (NDA) of the United States of America (USA). Details of study design have already been published elsewhere (March et al., 2004; TADS Team, 2003). All patients and at least one of their parents provided written informed consent. The Duke University Medical Center (Durham, NC) and the institutional review boards at each study site approved and monitored the study protocol; TADS was monitored quarterly by the data safety and monitoring board of

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the NIMH.

Study population

Detailed descriptions have already been published of TADS-subject eligibility criteria, subject

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recruitment, sample size calculation, and data collection, as well as a detailed description of the TADS sample, including its comparison against both clinical and epidemiological samples (March et al., 2004; TADS Team, 2003, 2005). The final sample consisted of 439 adolescents,

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recruited at 13 clinical sites, who met the criteria for major depressive disorder, as defined in the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV)

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(American Psychiatric Association, 1994). All patients were 12-17 years old, and 54.4% were female. In terms of racial background, 73.8% were Caucasian, 12.5% were African American,

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8.9% were Hispanic, and 4.8% were other. Socio-economically, 61.2% reported family

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incomes of $40,000 or higher over the preceding 12 months. Almost half (47.8%) had at least

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one coexisting DMS-IV disorder.

Interventions

Detailed descriptions of the interventions offered in TADS have been published previously (March et al., 2004; TADS Team, 2003). The acute phase trial included four treatment arms: a pill placebo (PBO); the active drug, fluoxetine (FLX); cognitive-behavioral therapy (CBT); and a combination of FLX and CBT (COMB). Eligible patients were randomly assigned to one of the four treatments in a 1:1:1:1 allocation ratio (PBO: n = 112, FLX: n = 109, CBT: n = 9

ACCEPTED MANUSCRIPT 111, COMB: n = 107), with computerized randomization stratified by study site and subject gender (March et al., 2004).

Outcomes Depression was assessed using the Children‘s Depression Rating Scale-Revised (CDRS-R),

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both at baseline and at week 12 by independent evaluators blinded to treatment arm assignments (March et al., 2004). The CDRS-R is a validated, clinician-administered rating scale that integrates information collected interviewing both the adolescent and a parent

(Mayes et al., 2010; Poznanski and Mokros, 1995). The raw summary score was used as a

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measure of depression (Curry et al., 2006; March et al., 2004).

To satisfy our primary study aim, we analyzed the patients‘ response to treatment rather than

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the summary score itself. As an index of each patient‘s response level, we used his or her proportional change in depression from baseline to the end of acute phase, since this measure

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is intuitive and routinely used to define ―response‖ in clinical trials (Ali and Lam, 2011; Emslie et al., 2010; Moller et al., 2012; Tao et al., 2009; Usala et al., 2008). Note that we did

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not dichotomize this measure around some threshold (usually 50%); instead, we used raw

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proportions to estimate treatment effects across the entire spectrum of response levels. The quantile treatment effect was then the difference between the response levels of the treatment

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and the placebo group at a particular quantile. For example, the treatment effect at the 50th quantile (i.e. the median) was the difference between the median of response levels in the treatment group and the median of response level in the placebo group.

Adjustment variables Adjustment variables included baseline depression scores, study site, and patients‘ treatment expectancies. Baseline depression was included to control for regression to the mean in the 10

ACCEPTED MANUSCRIPT response levels (Laird, 1983; Vickers and Altman, 2001), whereas study site was included to control for study site effects (March et al., 2004). The patients‘ treatment expectancies were included because it was impossible to totally blind all the subject groups to their treatment arm (those receiving CBT, either alone or in combination with FLX, invariably knew this) (March et al., 2004). Furthermore, the study did not include a CBT-plus-PBO-arm. As a

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result, treatment expectancies could be a confounder of treatment effectiveness (Weersing and Brent, 2006).

Before randomization, patients‘ expectancies as to how much they would improve if

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randomized to each of the three active treatments were assessed separately for FLX, CBT, and COMB (Curry et al., 2006). A single expectancy score was generated by assigning each patient the expectancy score corresponding to the treatment he or she actually received (Curry

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et al., 2006). Patients in the placebo condition were assigned their expectancy about FLX

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(Curry et al., 2006).

Missing values

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Missing values for the outcome measure were replaced by the scores predicted by the random

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coefficient regression model used in the original TADS analyses. These predicted scores were included in the TADS data set and previously used to deal with missing depression scores

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(Curry et al., 2006). According to the initial authors, the predicted-score imputation method provides a less biased estimate of the treatment effect than the last carried forward approach. In TADS, 61 of 439 patients (13.9%) had a missing outcome value.

Missing values for patients‘ treatment expectancies were imputed using the method proposed by Stekhoven and Bühlmann (2012). This method is based on machine learning and was

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ACCEPTED MANUSCRIPT shown to be comparative to established imputation methods. Missing expectancy values were replaced for 36 of 439 (8.2%) patients.

Statistical analysis The intention-to-treat principle was used for all analyses. We first calculated the quantiles of

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each treatment arm‘s response levels and used quantile-quantile-plots to provide unadjusted treatment-placebo-comparisons. In a quantile-quantile-plot, the corresponding quantiles of two distributions are plotted against each other. If the distributions do not differ, the quantiles of the two distributions do not differ and, hence, all values lie on the diagonal of the plot. In

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contrast, if the distributions differ in any way, the values deviate from the diagonal

accordingly. Quantile-quantile-plots thereby provide a comprehensive summary of a

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treatment-placebo-difference across the entire range of response levels.

We then employed quantile regression models to estimate treatment effects across response

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levels. To provide a comprehensive assessment of each treatment effect across the entire range of response levels, we modelled the 1st to the 99th quantiles with intervals of 1 (i.e., the

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1st, 2th, 3th, …, 99th quantiles). Assessing such a set of quantiles produces a curve that shows

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each treatment effect as a function of the quantiles. The question then is whether the curves of the treatments differ from one another. To quantify differences between the various

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treatments‘ curves, we calculated areas under the curve (AUC), and did so for both above(positive area) and below-zero values (negative area) on the y-axis. As a result, curves containing values less than zero were penalized.

To derive confidence intervals (CI) for the AUCs, we used the non-parametric bootstrap procedure, which allowed us to empirically estimate the probability distribution of an arbitrary statistical quantity (Efron and Tibshirani, 1994). Bootstrapping was stratified by 12

ACCEPTED MANUSCRIPT treatment condition (Efron and Tibshirani, 1994) and encompassed 3000 replications. We then used the ―bias-corrected and accelerated‖ method to calculate 95% CIs; this is the recommended method for deriving general-purpose bootstrap CIs (Efron and Tibshirani, 1994).

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To formally test for differences between the treatment arms‘ AUCs, we used the samples of paired AUC values generated by the bootstrap procedure. That is, for each bootstrap iteration, an AUC was calculated for CBT, FLX, and COMB, resulting in an estimated AUC for each treatment within that particular bootstrap sample. We compared these paired AUCs via paired

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t-tests (one-tailed), accompanied by Cohen‘s d for paired samples as an effect size measure. Note that, due to the large sample size of 3000 in these tests, the p-values are non-informative and all interpretations of results should be based on the effect sizes. We examined the

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following comparisons: a) CBT versus FLX

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b) CBT versus COMB

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c) FLX versus COMB

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We estimated two quantile regression models. Model 1 included the treatment effects adjusted for baseline depression. In model 2, we additionally adjusted for study site and patients‘

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treatment expectancies. Analyses were conducted using the statistical software program R, version 3.3.2 (R Core Team, 2016). Quantile regressions were estimated via the add-on package ―quantreg‖ (Koenker, 2015). AUCs were estimated using the trapezoid rule, as implemented in the package ―flux‖ (Jurasinski et al., 2014). Bootstrapping was done using the package ―boot‖ (Canty and Ripley, 2016).

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ACCEPTED MANUSCRIPT Results Table 1 provides the 10th to 90th response level quantiles of each treatment arm. Response levels were rather dispersed. In the placebo condition, the response level on the 10th quantile was 0.067 and as high as 0.61 on the 90th quantile. This corresponds to a reduction of the baseline depression score by 6.7% on the 10th quantile and by 60.1% on the 90th quantile.

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Similar ranges of response levels were found for CBT and FLX. In contrast, COMB had clearly higher response levels, achieving a response level of 0.184 – and thus a reduction of the baseline depression score by 18.4% - already on the 10th quantile. COMB also had the

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highest response level across all treatment arms on the 90th quantile, with a level of 0.667.

Figure 1 shows quantile-quantile-plots of all treatment-placebo-comparisons. As can be seen, the PBO vs. CBT response levels hardly differed, as indicated by the fact that the vast

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majority of quantile values lied on the diagonal of the plot. In contrast, the FLX quantiles were higher than the PBO quantiles at the middle range of response levels, whereas no

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differences were present among low and high quantiles. Finally, throughout most of the range of the response level distribution, the COMB quantiles were higher than the PBO quantiles.

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Only at the higher end the difference tapered off. Thus, considering the unadjusted quantiles,

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only COMB clearly outperformed PBO. FLX showed only some sign of effectiveness in the

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middle range of response levels, whereas CBT performed similarly to PBO.

The upper half of Figure 2 depicts the quantile treatment effects estimated for model 1. Every 5th quantile is shown to increase the figure‘s clarity (complete results are available in the online supplementary material, Tables S1-2). The picture gained from the unadjusted analyses provided in table 1 and figure 1 is largely confirmed: 1. CBT was no more effective than placebo across the entire range of response levels.

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ACCEPTED MANUSCRIPT 2. FLX was effective at the middle range of response levels, however, the effect of FLX was restricted to a rather small range of response levels, spanning from approximately the 24th and 66th quantiles. 3. COMB outperformed PBO across a large range of response levels, spanning from roughly the 2th to the 89th quantiles.

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Thus, whereas both FLX and COMB exhibited some level of effectiveness, COMB was effective across a broader range of response levels than FLX. Further adjusting the treatment effects for study site and the patients‘ treatment expectancies (model 2) produced the results in the lower half of Figure 1, where the general picture remains the same, except that the

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adjustments diminished COMB‘s effectiveness among higher-level responders.

Comparing the AUCs confirmed that there was an order of effectiveness among the

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treatments, with COMB the most effective (table 2). Specifically, the comparisons derived from model 1 indicate that FLX was more effective than CBT (Cohen‘s d: 2.09, 95% CI:

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2.03-2.16) and COMB was more effective than both FLX (Cohen‘s d: 2.56, 95% CI: 2.492.63) and CBT (Cohen‘s d: 5.09, 95% CI: 4.99-5.19, table 2). Considering model 2, the

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difference between COMB and FLX was less pronounced, but COMB still clearly

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outperformed FLX (Cohen‘s d: 1.39, 95% CI: 1.33-1.45), in line with Figure 1. Note that the Cohen‘s d-values correspond to what is commonly considered a ―large effect‖ (Bech, 2015;

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Leucht et al., 2012; Weisz et al., 2013; Weisz et al., 2006).

Additional analyses We conducted some additional analyses to examine the robustness of our results. First, we calculated the AUC after truncating the COMB quantile treatment effects at the average of the three highest FLX effects. This was done to assure that the high AUC of COMB was not simply due to a small number of large COMB effects. Second, we tested the AUC comparison 15

ACCEPTED MANUSCRIPT at a stricter level, by calculating the AUCs for the lower bound of the 95% CIs, rather than for the effects themselves. As can be seen in Table 3, these additional analyses fully confirmed the above-reported findings.

Finally, some authors have claimed that proportional change may be a problematic outcome

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(Curran-Everett and Williams, 2015; Vickers, 2001). Alternative approaches are to use the absolute change or to use analysis of co-variance (ANCOVA) predicting the end-of-trial

outcome by the treatment conditions, with the outcome‘s baseline values as an adjustment variable (Curran-Everett and Williams, 2015; Laird, 1983; Nash et al., 2014; Senn, 2006).

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We, therefore, repeated our analysis using both the change score and an ANCOVA approach. The results of these analyses are available in the online supplementary material (Figures S1-2,

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Tables S3-4). They confirmed all the above-reported findings.

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Discussion

In the TADS sample, whereas cognitive-behavioral therapy was found to be no more effective

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than placebo across response levels, our analyses revealed that combining cognitive-

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behavioral therapy and fluoxetine was not only effective, but had a more comprehensive effectiveness than fluoxetine used alone. This was indicated by higher AUCs for the

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combined-treatment effects, reflecting simultaneously that these effects were greater in magnitude and spread out across a broader range of response levels than for either treatment used alone. The latter point is of special importance, as it means that the combined-treatment was able to outperform a pill placebo even among low and high responders, whereas fluoxetine alone had a more restricted breadth of effectiveness.

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ACCEPTED MANUSCRIPT An important question arising from our analysis is: who are those patients with low and high response levels? Some of the high response levels might have been due to measurement errors; i.e., unusually high or low depression scores at baseline or unusually low scores at week 12. Note, however, that we included baseline depression as a covariate to adjust for such cases. In addition, some might be dropouts for which there were no actual end-of-trial data.

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Some low and high response levels, on the other hand, are likely to be legitimate. For example, treatment-resistant and chronic depression might give rise to lower levels of

response (Emslie et al., 2010). Some of the low responses might also reflect adverse reactions to the medication, resulting in treatment stoppage or aggravated depression in the medication

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groups. It is also likely that the depression level of some patients would have changed

regardless of their enrollment in the trial; for example, due to spontaneous remission, or secondary to positive or negative life events within the study period, which either facilitated

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or impeded a given treatment‘s effectiveness, respectively (Wilkinson et al., 2009). Finally,

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effect modifiers might have impacted patients‘ levels of response (Curry et al., 2006).

It is unlikely that any of these mechanisms is solely responsible for creating low or high

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response levels. Rather, low- and high-level responses are likely mechanistically

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multifactorial. The crucial point about these mechanisms is that they are extraneous to the trial – or more generally the clinical care a patient gets – and, hence, not under the treating

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clinicians‘ full control. It seems reasonable to assume that there are many such extraneous mechanisms that shape a patient‘s illness course, either because they impede or boost healing. Such mechanisms might then act as limiting factors of what can be achieved by any treatment, thereby creating low and high responders.

If treatment effectiveness is constrained by such limiting factors, our finding that combining cognitive-behavioral therapy and fluoxetine was able to exert an additional positive effect 17

ACCEPTED MANUSCRIPT within low and high responding groups becomes even more clinically relevant, as this treatment apparently was more robust than either therapy used alone. In particular, the finding that the combined treatment had also an effect on low response levels implies that it showed some robustness against factors that hampered recovery. It seems that the treatment was capable of extending the boundary of ―being treatable‖, in contrast to the more constrained

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effectiveness of fluoxetine used alone. The combined treatment can therefore be expected to work in a larger set of depressed adolescents and might allow for effectively treating some of the adolescents for whom the drug alone does not provide relief.

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The combination of cognitive-behavioral therapy and fluoxetine was already found to be the most useful treatment in the TADS study, in several regards: a) it was the most effective acute-phase treatment (March et al., 2004); b) its effectiveness was robust against effect

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modifiers (Curry et al., 2006); and c) it had the best benefit-to-harm ratio (March et al., 2007). Our results provide additional justification for using a combined cognitive-behavioral

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therapy-antidepressant medication approach, in that the combination of cognitive-behavioral therapy and fluoxetine was a robust treatment approach that outperformed placebo in a

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broader range of depressed adolescents than the corresponding monotherapies. For the

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treating clinician, these findings suggest that combining cognitive-behavioral therapy and an antidepressant is more likely to provide some relief for the patient than either therapy alone.

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The combined treatment thereby seems to be a powerful option for treating adolescents with moderate to severe depression.

On the other hand, the literature comparing combined depression treatments to corresponding monotherapies for adolescents is inconclusive (Brent et al., 2008; Cox et al., 2014; Dubicka et al., 2010; Goodyer et al., 2008; Hetrick et al., 2011; Ma et al., 2014; Melvin et al., 2006). Similarly, the existing evidence concerning the effectiveness of SSRIs in depressed 18

ACCEPTED MANUSCRIPT adolescents is inconclusive (Bridge et al., 2007; Cheung et al., 2008; Cipriani et al., 2016; Cox et al., 2012; Emslie et al., 2004; Emslie et al., 2008; Findling et al., 2013; Hetrick et al., 2012; Le Noury et al., 2015, 2016; Masi et al., 2010; Thapar et al., 2012; Usala et al., 2008; Wagner et al., 2003; Wagner et al., 2006; Wagner et al., 2004; Whittington et al., 2004), though fluoxetine – the antidepressant used in the TADS trial – is the SSRI that has received

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the most consistent empirical support (Cipriani et al., 2016; Masi et al., 2010; Usala et al., 2008). The negative TADS results for cognitive-behavioral therapy were surprising, as cognitive-behavioral therapy has usually been found effective and was deemed a well-

established treatment for adolescent depression (Harrington et al., 1998; Hollon et al., 2005;

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Klein et al., 2007; Masi et al., 2010; Reinecke et al., 1998; Weersing and Brent, 2006; Weersing et al., 2017; Weersing et al., 2009; Weisz et al., 2006).

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The overall picture is, thus, inconclusive for cognitive-behavioral therapy alone, an antidepressant alone, and the combination in adolescents, meaning that further research is

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needed for clarification. We are also not aware of any other analysis comparable to ours. The degree to which our findings are replicable must therefore be left for future research. Note,

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however, that TADS was designed to be an effectiveness trial. Accordingly, a sample

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covering the full range of treatment-seeking adolescents in the USA was recruited. It was, therefore, suggested that the TADS results should be broadly applicable to youths seeking

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treatment for depression in the USA (Hollon et al., 2005; March et al., 2004; TADS Team, 2005). We see no reason why this suggestion should not also apply to our findings, especially considering that our ―average‖ results were in line with the conclusions drawn by the TADS team, based upon their own acute-phase analyses (March et al., 2004). As such, our results might be generalizable, at least to adolescents who are comparable to the TADS sample.

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ACCEPTED MANUSCRIPT Limitations of the TADS trial have been noted before (Curry et al., 2006; March et al., 2004) and our analyses do not transcend these limitations. Some additional issues should also be noted. First, commonly-used depression rating scales have been criticized for not being psychometrically valid (Bech, 2010, 2015; Fried and Nesse, 2015; Fried et al., 2016) and this applies to the CDRS-R, as well (Isa et al., 2014). Measurement error might, therefore, have

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clouded both the previous and our analyses. Second, our analysis focused on effectiveness and did not include any harm-benefit evaluation. Third, estimating fine-grained quantile intervals of 1 was limited by the sample sizes of the TADS treatment arms that ranged from 107 to 112 patients. With these sample sizes, there were only around 1 to 3 additional patients per

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interval. If sample sizes are even smaller, larger intervals (e.g. every 5th or every 10th quantile) are more appropriate. Fourth, as with conventional statistical analyses, the quantile treatment effects apply only probabilistically to individual patients. Thus, although the combined

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treatment was found to work even at the low end of the response level distribution and to have the most consistent effectiveness, it cannot be inferred that the highest response of a particular

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patient will necessarily occur in the combined treatment or that an initially treatment-resistant patient will necessarily show a response when later treated with the combined treatment.

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Finally, our analysis was an ad-hoc analysis using data from a trial that was not originally

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designed to accommodate such analysis. Our results must, therefore, be replicated and

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validated in future studies.

Conclusion

We found that, in the TADS sample, the combination of cognitive-behavioral therapy and the SSRI fluoxetine was more effective than either treatment used alone, not just in average effectiveness, but in the breadth of patients in whom it was effective.

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ACCEPTED MANUSCRIPT Declarations of interest: None.

Funding source This research did not receive any specific grant from funding agencies in the public,

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commercial, or not-for-profit sectors.

Acknowledgements

Data used in the preparation of this manuscript were obtained and analysed from the

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controlled access datasets distributed from the National Institute of Mental Health (NIMH)supported National Database for Clinical Trials (NDCT, https://dataarchive.nimh.nih.gov/ndct/). NDCT is a collaborative informatics system created by the

M

NIMH to provide a national resource to support and accelerate discovery related to clinical trial research in mental health. Dataset identifier: Clinical Trials #2145. This manuscript

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reflects the views of the authors and may not reflect the opinions or views of the NIMH or of

PT

the submitters submitting original data to NDCT.

CE

Author statement

AC

Contributors

Conceptualization of study: SF and MMK. Data acquisition and preparation: SF. Statistical analysis: SF. Interpretation of data: SF and MMK. Drafting the manuscript: SF. Revising the manuscript for important intellectual content: MMK. All authors have approved the final manuscript.

Funding source 21

ACCEPTED MANUSCRIPT This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Acknowledgements Data used in the preparation of this manuscript were obtained and analysed from the

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controlled access datasets distributed from the National Institute of Mental Health (NIMH)supported National Database for Clinical Trials (NDCT, https://data-

archive.nimh.nih.gov/ndct/). NDCT is a collaborative informatics system created by the

NIMH to provide a national resource to support and accelerate discovery related to clinical

AN US

trial research in mental health. Dataset identifier: Clinical Trials #2145. This manuscript

reflects the views of the authors and may not reflect the opinions or views of the NIMH or of

M

the submitters submitting original data to NDCT.

ED

Declarations of interest:

AC

CE

PT

None.

22

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ACCEPTED MANUSCRIPT Whittington, C.J., Kendall, T., Fonagy, P., Cottrell, D., Cotgrove, A., Boddington, E., 2004. Selective serotonin reuptake inhibitors in childhood depression: systematic review of published versus unpublished data. Lancet (London, England) 363, 1341-1345. Wilcox, R., 2012. Introduction to robust estimation and hypothesis testing, 3. ed. Academic Press, Amsterdam; Boston.

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of mental science 194, 334-341.

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Quantile

PBO

CBT

FLX

COMB

10th

0.067

0.068

0.040

0.184

20 th

0.135

0.148

0.146

0.312

30 th

0.197

0.214

0.257

0.356

40 th

0.239

0.264

0.378

0.412

50 th

0.298

0.312

0.403

0.439

60 th

0.369

0.364

0.455

0.492

70 th

0.418

0.423

0.515

0.549

80 th

0.498

0.480

0.537

0.612

90 th

0.610

0.560

0.621

0.667

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Table 1. Quantiles of response levels in placebo and treatment conditions.

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PBO = Placebo; CBT = Cognitive-behavioral therapy; FLX = Fluoxetine; COMB = Combination of CBT and FLX

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Response levels were defined as the proportional change in depression from baseline to end of acute phase. For example, a response level of 0.067

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means that the baseline depression score was reduced by 6.7%.

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Table 2. Areas under the curves of quantile treatment effects and comparisons of the areas under the curves between treatments. condition Model 1

Model 2

Treatments AUC

t-value

df

p-value

Cohen’s d

95% CI

-114.71

2999

< 0.00001

2.09

2.03 - 2.16

0.013 - 0.121 CBT vs. COMB

-278.81

2999

< 0.00001

5.09

4.99 - 5.19

0.080 - 0.177 FLX vs. COMB

-140.16

2999

< 0.00001

2.56

2.49 - 2.63

-136.48

2999

< 0.00001

2.49

2.42 - 2.56

0.023 - 0.129 CBT vs. COMB

-220.76

2999

< 0.00001

4.03

3.94 - 4.12

0.059 - 0.163 FLX vs. COMB

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Treatment

-76.10

2999

< 0.00001

1.39

1.33 - 1.45

95% CI

CBT

0.006

FLX

0.065

COMB

0.131

CBT

0.009

FLX

0.076

COMB

0.113

compared

-0.045 - 0.057 CBT vs. FLX

-0.043 - 0.064 CBT vs. FLX

AUC = Area under the curve; CBT = Cognitive-behavioral therapy; FLX = Fluoxetine; COMB = Combination of CBT and FLX; CI = Confidence interval.

Treatment effects were adjusted for baseline depression severity in model 1 and additionally for study site and patients‘ treatment expectancies in

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model 2.

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Table 3. Areas under the curves of quantile treatment effects and comparisons of the areas under the curves between treatments: Results of additional analyses. Model

Type of analysis

AUC

95% CI

Treatments compared

t-value

df

p-value

Cohen’s d

95% CI

CBT

0.006

-0.045 - 0.057

CBT vs. FLX

-114.71

2999

< 0.00001

2.09

2.03 - 2.16

FLX

0.065

0.013 - 0.121

CBT vs. COMB truncated

-265.31

2999

< 0.00001

4.84

4.74 - 4.94

COMB truncated

0.111

0.061 - 0.153

FLX vs. COMB truncated

-169.86

2999

< 0.00001

3.10

3.03 - 3.18

CBT lower-CI

-0.073

-0.125 - -0.021

CBT lower-CI vs. FLX lower-CI

-98.16

2999

< 0.00001

1.79

1.73 - 1.85

FLX lower-CI

-0.017

-0.074 - 0.041

CBT lower-CI vs. COMB lower-CI

-270.06

2999

< 0.00001

4.93

4.83 - 5.03

COMB lower-CI

0.054

0.001 - 0.102

FLX lower-CI vs. COMB lower-CI

-141.19

2999

< 0.00001

2.58

2.51 - 2.65

CBT

0.009

-0.043 - 0.064

CBT vs. FLX

-136.48

2999

< 0.00001

2.49

2.42 - 2.56

FLX

0.076

0.023 - 0.129

CBT vs. COMB truncated

-208.36

2999

< 0.00001

3.80

3.72 - 3.89

COMB truncated

0.1

0.047 - 0.146

FLX vs. COMB truncated

-75.62

2999

< 0.00001

1.38

1.32 - 1.44

CBT lower-CI

-0.07

-0.124 - -0.014

CBT lower-CI vs. FLX lower-CI

-124.78

2999

< 0.00001

2.28

2.21 - 2.34

FLX lower-CI

-0.004

-0.061 - 0.052

CBT lower-CI vs. COMB lower-CI

-214.58

2999

< 0.00001

3.92

3.83 – 4.00

COMB lower-CI

0.037

-0.016 - 0.089

FLX lower-CI vs. COMB lower-CI

-77.11

2999

< 0.00001

1.41

1.35 - 1.46

Treatment

truncated a

CI b

Model 2

truncated a

CI b

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Model 1

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condition

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AUC = Area under the curve; CBT = Cognitive-behavioral therapy; FLX = Fluoxetine; COMB = Combination of CBT and FLX; CI = Confidence interval.

Treatment effects were adjusted for baseline depression severity in model 1 and additionally for study site and patients‘ treatment expectancies in

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model 2. a

In this analysis, the AUC for COMB was calculated after truncating the COMB-coefficients at the average of the three highest FLX-coefficients.

b

In this analysis, for each treatment, the AUC of the lower bounds of the 95% confidence intervals rather than that of the coefficients themselves

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CE

was calculated.

35

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Figure legends

Figure 1. Quantile-quantile-plots of the response level distributions of all treatments against placebo

CBT = Cognitive-behavioral therapy; FLX = Fluoxetine; COMB = Combination of CBT and FLX.

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A quantile-quantile-plot plots the corresponding quantiles of two distributions against each other, e.g. the 50th quantile against the 50th quantile (i.e. the median of the first distribution against the median of the second distribution). If the two distributions do not differ, then the values at each quantile are the same (e.g. the median response level is the same in PBO and CBT) and hence, lie on the diagonal of the plot. Otherwise, the values lie below or above the diagonal. For example, COMB‘s quantiles are consistently higher than the PBO quantiles throughout most of the range of the response levels, meaning that COMB produced consistently higher response levels than PBO.

Response levels were defined as the proportional change in depression from baseline to end of acute phase. For example, a response level of 0.5

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ED

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means that the baseline depression score was reduced by 50%.

36

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Figure 2. Treatment effects with 95% confidence intervals as a function of quantiles estimated by model 1 (upper part) and model 2 (lower part)

CBT = Cognitive-behavioral therapy; FLX = Fluoxetine; COMB = Combination of CBT and FLX; CI = Confidence Interval.

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The treatment effect is the difference between the response levels of the treatment and the placebo group, adjusted for baseline depression severity in model 1 (upper part of figure) and additionally for study site and patients‘ treatment expectancies in model 2 (lower part of figure). For example, the treatment effect at the 50th quantile is the (adjusted) difference between the median response level in the placebo group and the median response

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level in the treatment group.

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