Gender as Predictor and Moderator of Outcome in Cognitive Behavior Therapy and Pharmacotherapy for Adult Depression: An “Individual Patient Data” Meta-Analysis

Cuijpers, Pim; Weitz, Erica; Twisk, Jos W. R.; Kuehner, Christine; Cristea, Ioana A.; David, Daniel; DeRubeis, Robert J.; Dimidjian, Sona; Dunlop, Boadie W.; Faramarzi, Mahbobeh; Hegerl, Ulrich; Jarrett, Robin B.; Kennedy, Sidney H.; Kheirkhah, Farzan; Mergl, Roland; Miranda, Jeanne; Mohr, David C.; Segal, Zindel V.; Siddique, Juned; Simons, Anne D.; Vittengl, Jeffrey R.; Hollon, Steven D.

doi:10.1002/da.22328

Cited by 127 publications

(66 citation statements)

References 41 publications

(47 reference statements)

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“…All models were fitted using data of the 694 patients from seven studies, for which complete data was available. Results of our analysis may therefore not be fully representative of the complete dataset of the meta-analysis by Cuijpers et al (2014).…”

Section: Application: Individual Patient-level Meta-analysis On Treatmentioning

confidence: 89%

“…Treatment outcomes were assessed by means of the 17-item Hamilton Rating Scale for Depression (HAM-D; Hamilton, 1960). Cuijpers et al (2014) found no indication that gender predicted or moderated treatment outcome. In our analyses, post-treatment HAM-D score was the outcome variable, and potential partitioning variables were age, gender, level of education, presence of a comorbid anxiety disorder at baseline, and pre-treatment HAM-D score.…”

Section: Application: Individual Patient-level Meta-analysis On Treatmentioning

confidence: 96%

“…Dataset To illustrate the use of GLMM trees in real data applications, we employ a dataset from an individual-patient data meta-analysis of Cuijpers et al (2014). This metaanalysis was based on patient-level observations from 14 RCTs, comparing the effects of psychotherapy (cognitive behavioral therapy; CBT) and pharmacotherapy (PHA) in the treatment of depression.…”

Section: Application: Individual Patient-level Meta-analysis On Treatmentioning

confidence: 99%

“…This metaanalysis was based on patient-level observations from 14 RCTs, comparing the effects of psychotherapy (cognitive behavioral therapy; CBT) and pharmacotherapy (PHA) in the treatment of depression. The study of Cuijpers et al (2014) was aimed at establishing whether gender is a predictor or moderator of the outcomes of psychological and pharmacological treatments for depression. Treatment outcomes were assessed by means of the 17-item Hamilton Rating Scale for Depression (HAM-D; Hamilton, 1960).…”

Section: Application: Individual Patient-level Meta-analysis On Treatmentioning

confidence: 99%

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Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Fokkema¹,

et al. 2017

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Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data metaanalysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.

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Section: Application: Individual Patient-level Meta-analysis On Treatmentioning

confidence: 89%

Section: Application: Individual Patient-level Meta-analysis On Treatmentioning

confidence: 96%

Section: Application: Individual Patient-level Meta-analysis On Treatmentioning

confidence: 99%

Section: Application: Individual Patient-level Meta-analysis On Treatmentioning

confidence: 99%

See 2 more Smart Citations

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Fokkema¹,

et al. 2017

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“…Whereas the current analyses focused on outcomes diverging from the mean, past studies using the individual patient database focused on averages and conventional outcomes [31–32]. Average treatment effects did not differ significantly between the 8 excluded versus 16 included studies, suggesting that the included studies are representative [32].…”

Section: Methodsmentioning

confidence: 99%

Divergent Outcomes in Cognitive-Behavioral Therapy and Pharmacotherapy for Adult Depression

Vittengl

Jarrett²,

Weitz

et al. 2016

AJP

Self Cite

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Objective Although the average depressed patient benefits moderately from cognitive-behavioral therapy (CBT) or pharmacotherapy, some experience divergent outcomes. We tested frequencies, predictors, and moderators of negative (deterioration, extreme non-response) and unusually-positive (superior improvement, superior response) symptomatic outcomes. Method Sixteen randomized clinical trials comparing CBT versus pharmacotherapy for unipolar depression provided individual patients’ (N=1700) Hamilton Rating Scale of Depression (HAM-D) and/or Beck Depression Inventory (BDI) scores pre- and post-treatment. We tested demographic and clinical predictors and treatment moderators of any deterioration (increases ≥1 HAM-D or BDI points), reliable deterioration (increases ≥8 HAM-D or ≥9 BDI points), extreme non-response (post-treatment HAM-D ≥21 or BDI ≥31), superior improvement (HAM-D or BDI decreases ≥95%), and superior response (post-treatment HAM-D or BDI =0) using multilevel models. Results About 5–7% of patients showed any deterioration, 1% reliable deterioration, 4–5% extreme non-response, 6–10% superior improvement, and 4–5% superior response on the HAM-D or BDI. Superior improvement on the HAM-D (OR=1.67) only and attrition (OR=1.67) were more frequent in pharmacotherapy versus CBT. Patients with deterioration and superior response had lower, whereas patients with extreme non-response and superior improvement had higher, symptoms pre-treatment. Conclusions Deterioration and extreme non-response (a) occur infrequently in randomized clinical trials comparing CBT versus pharmacotherapy for depression, and (b) mirror superior improvement and superior response in distributions of symptom changes and end-states, respectively. Pre-treatment symptom levels help forecast negative and unusually-positive outcomes but do not guide selection of CBT versus pharmacotherapy. Pharmacotherapy may produce clinician-rated superior improvement and attrition more frequently than does CBT.

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The Efficacy of Cognitive Behavioural Therapy for Depression

Jarrett¹,

Vittengl²

2015

Treating Depression

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Gender as Predictor and Moderator of Outcome in Cognitive Behavior Therapy and Pharmacotherapy for Adult Depression: An “Individual Patient Data” Meta-Analysis

Cited by 127 publications

References 41 publications

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Divergent Outcomes in Cognitive-Behavioral Therapy and Pharmacotherapy for Adult Depression

The Efficacy of Cognitive Behavioural Therapy for Depression

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