Clinical trial methodology and drug-placebo differences

Placebo response mitigation with a participant-focused psychoeducational procedure: a randomized, single-blind, all placebo study in major depressive and psychotic disorders

The remarkably high and growing placebo response rates in clinical trials for CNS indications, such as depression and schizophrenia, constitute a major challenge for the drug development enterprise. Despite extensive literature on participant expectancies and other potent psychosocial factors that perpetuate placebo response, no empirically validated participant-focused strategies to mitigate this phenomenon have been available. This study evaluated the efficacy of the Placebo-Control Reminder Script (PCRS), a brief interactive procedure that educates participants about factors known to cause placebo response, which was administered prior to the primary outcome assessments to subjects with major depressive or psychotic disorders who had at least moderate depression. Participants were informed they would participate in a 2-week randomized clinical trial with a 50% chance of receiving either an experimental antidepressant medication or placebo. In actuality, all participants received placebo. Participants randomly assigned to receive the PCRS (n = 70) reported significantly smaller reductions (i.e., less placebo response) in depression than those who did not receive the PCRS (n = 67). The magnitude of this effect was medium (Cohen's d = 0.40) and was not significantly impacted by diagnostic status. The number of adverse events (i.e., nocebo effect) was also lower in the PCRS group, particularly in the first week of the study. These findings suggest that briefly educating participants about placebo response factors can help mitigate the large placebo response rates that are increasingly seen in failed CNS drug development programs.

Raising Placebo Efficacy in Antidepressant Trials Across Decades Explained by Small-Study Effects: A Meta-Reanalysis

BACKGROUND

Recent meta-analyses reported placebo response rate in antidepressant trials to be stable since the 1970s. These meta-analyses however were limited in considering only linear time trends, assessed trial-level covariates based on single-model hypothesis testing only, and did not adjust for small-study effects (SSE), a well-known but not yet formally assessed bias in antidepressant trials.

METHODS

This secondary meta-analysis extends previous work by modeling nonlinear time trends, assessing the relative importance of trial-level covariates using a multimodel approach, and rigorously adjusting for SSE. Outcomes were placebo efficacy (continuous), based on the Hamilton Depression Scale, and placebo response rate.

RESULTS

Results suggested that any nonlinear time trends in both placebo efficacy (continuous) and response rate were best explained by SSE. Adjusting for SSE revealed a significant gradual increase in placebo efficacy (continuous) from 1979 to 2014. A similar observation was made for placebo response rate, but did not reach significance due higher susceptibility to SSE. By contrast, trial-level covariates alone were found to be insufficient in explaining time trends.

CONCLUSION

The present findings contribute to the ongoing debate on antidepressant placebo outcomes and highlight the need to adjust for bias introduced by SSE. The results are of clinical relevance because SSE may affect the evaluation of success or failure in antidepressant trials.

Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits.

METHODS

Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics.

RESULTS

Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58).

CONCLUSIONS

A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.