Evaluation of Differences in Individual Treatment Response in Schizophrenia Spectrum Disorders

Winkelbeiner, Stephanie; Leucht, Stefan; Kane, John M.; Homan, Philipp

doi:10.1001/jamapsychiatry.2019.1530

Cited by 72 publications

(121 citation statements)

References 70 publications

Supporting

Mentioning

114

Contrasting

Order By: Relevance

“…However, these trials can only identify mean differences between treatment arms (ie, efficacy), whereas variation between patients, within patients, as well as patient-by-treatment interactions are part of the error term. Nevertheless, if patient-by-treatment interaction effects are present, then the variance in the treatment outcome should be increased in the drug group relative to the placebo group, because no comparable drug-by-patient interaction is present in the placebo group 10 16 17. Thus, results from RCT can inform indirectly if there might be subgroups of benefiters.…”

Section: Introductionmentioning

confidence: 99%

What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis

Plöderl

Hengartner

2019

BMJ Open

View full text Add to dashboard Cite

ObjectivesTo investigate if the treatment effect of antidepressants in patients with depression substantially varies in each patient (patient-by-treatment interaction or treatment heterogeneity), a necessary but largely unexplored prerequisite of personalised antidepressant treatment.DesignMeta-analytic variance comparison of treatment outcome between drug arms and placebo arms of clinical trials, based on the assumption that patient-by-treatment interaction should lead to larger variances in drug arms than placebo arms. To put the results into context, we run simple simulations, assuming different definitions and rates of those who respond especially well to antidepressants.Data sources163 randomised, placebo-controlled trials (51 396 patients) with complete results for pre–post differences, selected from a recently published systematic review.AnalysisVariance ratios (VRs) and coefficients of variance ratios (CVRs) of individual trials were meta-analytically combined. The analysis was repeated for classes of antidepressants and specific antidepressants.ResultsVRs (VR=1.01, CI 0.99 to 1.02) and CVRs (CVR=0.82, CI 0.80 to 0.84) of the antidepressant-treatment arms were comparable or smaller than in placebo arms. Similar results were observed for classes of antidepressants and for specific antidepressants. Our simulation analysis confirmed that equal VRs can only be obtained if they are not more than a few patients who respond slightly above average.ConclusionsThe lack of increased treatment-outcome variance in the antidepressants versus placebo groups in randomised controlled trials indicates that no or only very small subgroups of patients respond particularly well to antidepressants. Thus, the scope for personalised treatment with antidepressants seems to be limited.

show abstract

Section: Introductionmentioning

confidence: 99%

What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis

Plöderl

Hengartner

2019

BMJ Open

View full text Add to dashboard Cite

show abstract

“…Differences in variance could be caused by many factors. One is the existence of patient characteristics that influence the effectiveness of the intervention (effect modifiers), which could manifest as subgroups between which the intervention (or control) treatments have different effects [8]. For example, the intervention may have a different effect in those with worse (or better) values at baseline, or outcomes in the control arm may vary due to differences in "usual practice".…”

Section: Discussionmentioning

confidence: 99%

“…Estimates that are accompanied by SEs can readily be meta-analysed using standard methods (here, the VD and CVD methods). RoV, logSDR and logCVR can be meta-analysed using bespoke methods using a random effects model with restricted maximum likelihood estimates (REML) of the ratios (RoV [19]; logSDR and logCVR [8,25]). We note that if variances within arms are very different across trials in a meta-analysis, ratio methods may be preferable.…”

Section: Methods For Use With Summary Data From Meta-analysesmentioning

confidence: 99%

“…Previous analyses have implied that CoV should only be explored in a meta-analysis if the SDs and means within each trial arm are correlated [8,15]. However, by simulating trial data (Supplementary Material §3), with (A) same CoV and (B) different CoV in the arms, we have shown that the correlation of the mean and SD from individual trials is not necessarily indicative of the CoV or whether the CoV differs between arms of the trial (Figures A1&A2).…”

Section: Methods For Use With Summary Data From Meta-analysesmentioning

confidence: 99%

“…However, just as meta-analysis of mean effects gives greater power to detect an average intervention effect, meta-analysis of differences in variance should give increased power to detect effect modification. A small number of meta-analyses in epidemiology and ecology have reported on differences in variance [8,11,[13][14][15][16][17][18][19][20][21], with applications to RCTs and other types of comparative study. Most of them found evidence of a difference in variance between arms, with varying strength of evidence (Table A1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting heterogeneity of intervention effects using analysis and meta-analysis of differences in variance between arms of a trial

Mills

Higgins

Morris

et al. 2020

Preprint

View full text Add to dashboard Cite

Randomised controlled trials (RCTs) with continuous outcomes usually only examine mean differences in response between trial arms. If the intervention has heterogeneous effects (e.g. the effect of the intervention differs by individual characteristics), then outcome variances will also differ between arms. However, power of an individual trial to assess heterogeneity is lower than the power to detect the same size of main effect. The aim of this work was to describe and implement methods for examining heterogeneity of effects of interventions, in trials with individual patient data (IPD) and also in meta-analyses using summary data. Several methods for assessing differences in variance were applied using IPD from a single trial, and summary data from two meta-analyses.In the single trial there was agreement between methods, and the difference in variance was largely due to differences in depression at baseline. In two meta-analyses, most individual trials did not show strong evidence of a difference in variance between arms, with wide confidence intervals. However, both meta-analyses showed evidence of greater variance in the control arm, and in one example this was perhaps because mean outcome in the control arm was higher.Low power of individual trials to examine differences in variance can be overcome using meta-analysis. Evidence of differences in variance should be followed-up to identify potential effect modifiers and explore other possible causes such as varying compliance.

show abstract

Heterogeneity of Treatment Effects in Internet- and Mobile-Based Interventions for Depression

Terhorst,

Kaiser,

Brakemeier

et al. 2024

JAMA Netw Open

View full text Add to dashboard Cite

ImportanceWhile the effects of internet- and mobile-based interventions (IMIs) for depression have been extensively studied, no systematic evidence is available regarding the heterogeneity of treatment effects (HTEs), indicating to what extent patient-by-treatment interactions exist and personalized treatment models might be necessary.ObjectiveTo investigate the HTEs in IMIs for depression as well as their efficacy and effectiveness.Data SourcesA systematic search in Embase, MEDLINE, Central, and PsycINFO for randomized clinical trials and supplementary reference searches was conducted on October 13, 2019, and updated March 25, 2022. The search string included various terms related to digital psychotherapy, depression, and randomized clinical trials.Study SelectionTitles, abstracts, and full texts were reviewed by 2 independent researchers. Studies of all populations with at least 1 intervention group receiving an IMI for depression and at least 1 control group were eligible, if they assessed depression severity as a primary outcome and followed a randomized clinical trial (RCT) design.Data Extraction and SynthesisThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Risk of bias was evaluated using the Cochrane Risk of Bias Tool. HTE was investigated using logarithmic variance ratios (lnVR) and effect sizes using Hedges g. Three-level bayesian meta-regressions were conducted.Main Outcomes and MeasuresHeterogeneity of treatment effects was the primary outcome of this study; magnitudes of treatment effect sizes were the secondary outcome. Depression severity was measured by different self-report and clinician-rated scales in the included RCTs.ResultsThe systematic review of 102 trials included 19 758 participants (mean [SD] age, 39.9 [10.58] years) with moderate depression severity (mean [SD] in Patient Health Questionnaire–9 score, 12.81 [2.93]). No evidence for HTE in IMIs was found (lnVR = −0.02; 95% credible interval [CrI], −0.07 to 0.03). However, HTE was higher in more severe depression levels (β̂ = 0.04; 95% CrI, 0.01 to 0.07). The effect size of IMI was medium (g = −0.56; 95% CrI, −0.46 to −0.66). An interaction effect between guidance and baseline severity was found (β̂ = −0.24, 95% CrI, −0.03 to −0.46).Conclusions and RelevanceIn this systematic review and meta-analysis of RCTs, no evidence for increased patient-by-treatment interaction in IMIs among patients with subthreshold to mild depression was found. Guidance did not increase effect sizes in this subgroup. However, the association of baseline severity with HTE and its interaction with guidance indicates a more sensitive, guided, digital precision approach would benefit individuals with more severe symptoms. Future research in this population is needed to explore personalization strategies and fully exploit the potential of IMI.

show abstract

Evaluation of Differences in Individual Treatment Response in Schizophrenia Spectrum Disorders

Cited by 72 publications

References 70 publications

What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis

What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis

Detecting heterogeneity of intervention effects using analysis and meta-analysis of differences in variance between arms of a trial

Heterogeneity of Treatment Effects in Internet- and Mobile-Based Interventions for Depression

Contact Info

Product

Resources

About