A Novel Methodology to Estimate the Treatment Effect in Presence of Highly Variable Placebo Response

Abstract

One of the main reasons for the inefficiency of multicenter randomized clinical trials (RCTs) in depression is the excessively high level of placebo response. The aim of this work was to propose a novel methodology to analyze RCTs based on the assumption that centers with high placebo response are less informative than the other centers for estimating the 'true' treatment effect (TE). A linear mixed-effect modeling approach for repeated measures (MMRM) was used as a reference approach. The new method for estimating TE was based on a nonlinear longitudinal modeling of clinical scores (NLMMRM). NLMMRM estimates TE by associating a weighting factor to the data collected in each center. The weight was defined by the posterior probability of detecting a clinically relevant difference between active treatment and placebo at that center. Data from five RCTs in depression were used to compare the performance of MMRM with NLMMRM. The results of the analyses showed an average improvement of~15% in the TE estimated with NLMMRM when the center effect was included in the analyses. Opposite results were observed with MMRM: TE estimate was reduced by~4% when the center effect was considered as covariate in the analysis. The novel NLMMRM approach provides a tool for controlling the confounding effect of high placebo response, to increase signal detection and to provide a more reliable estimate of the 'true' TE by controlling false negative results associated with excessively high placebo response.

Is It Time for Going Beyond the P‐Value Paradigm With the Estimation of the Probability of Clinical Benefit as a Criterion for Assessing the Outcomes of a Clinical Trial? A Case Study in Patients With Major Depressive Disorder

“…where A is the HAMD score at baseline, td is the time necessary to reduce the baseline value by 63.2%, b is the shape of the HAMD trajectory, and h is the recovery rate. The interindividual variability of the model parameters was assumed to be characterized by a log‐normal distribution, and the residual error was assumed to be normally distributed according to the previously conducted modeling results . This model was characterized by 2 components: an exponential term associated with the curvilinear decrement from a baseline score and a linear time‐dependent term (h·t) associated with the return to the baseline conditions when the treatment effect (placebo or active drug) disappears.…”

Depressive disorders: Treatment failures and poor prognosis over the last 50 years

“…In other words, the use of either MMRM or LOCF will lead to the same conclusions but MMRM is likely to yield fewer mis‐steps along the way according to some groups . An extension of the MMRM, the novel nonlinear NLMMRM provides a tool for assessing a weighting factor collected from various centres thereby controlling the confounding effect of high placebo response across sites, to increase signal detection and to provide a more reliable estimate of the “true treatment effect” (TE) by controlling false negative results associated with excessively high placebo …”

“…In summary, such studies strike at the core of our understanding of neuropsychopharmacology drug development. Indeed, there has been considerable debate as to the nature of these analyses and how they can be best interpreted to achieve success rather than failure . However, the single most comforting suggestion for psychopharmacology is that the powerful antidepressant effects of these drugs are actually masked by the inadequacy of current clinical trial designs and that the research strategy for the evaluation of novel psychotropic agents, according to Matthews and colleagues over a decade ago, needs significant rethinking and reevaluation .…”

Methods for assessing and controlling placebo effects

“…With varying placebo effects across centers, the estimated treatment effects may be unduly amplified when averaging across centers. To control for these varying placebo effects, Gomeni et al 42 propose a method for estimating treatment effects based on the idea that centers with large placebo effects are less informative for estimating the treatment effect than centers where participants experience little to no placebo effects. This information is included in the analysis of the outcome data using weights calculated as the inverse probability of detecting a relevant treatment effect.…”