A New Population-Enrichment Strategy to Improve Efficiency of Placebo-Controlled Clinical Trials of Antidepressant Drugs
Neurosciences Center of Excellence for Drug Discovery, GlaxoSmithKline R&D, Verona, Italy.Clinical Pharmacology & Therapeutics (Impact Factor: 7.9). 11/2010; 88(5):634-42. DOI: 10.1038/clpt.2010.159
The rate-limiting factor in the discovery of novel antidepressants is the inefficient methodology of traditional multicenter randomized clinical trials (RCTs). We applied a model-based approach to a large clinical database (five RCTs in major depressive disorder (MDD), involving 1,837 patients from 124 recruitment centers) with two objectives: (i) to learn about the role of center-specific placebo response in RCT failure and (ii) to apply what is learned to improve the efficiency of RCTs by enhancing the detection of treatment effect (TE). Sensitivity analysis indicated that center-specific placebo response was the most relevant predictor of RCT failure. To reduce the statistical "noise" generated by centers with nonplausible, excessively high/low placebo responses, we developed an enrichment-window strategy. Clinical trial simulation was used to assess the enrichment strategy applied before the standard statistical analysis, resulting in an overall reduction in failure of RCTs from ~50 to ~10%.
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- "MATERIALS AND METHODS Data Data from five RCTs were used in the analyses. The same data were previously analyzed for the assessment of the band-pass filter approach as a novel population enrichment strategy (Merlo-Pich et al, 2010). Data were derived from GSK clinical databases (GSK clinical trial register [http: "
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 non-linear 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 5 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.Neuropsychopharmacology accepted article preview online, 21 April 2015. doi:10.1038/npp.2015.105.
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ABSTRACT: In this issue, Merlo-Pich et al. present an enrichment-window approach that identifies sites with aberrant mean placebo responses and excludes data from those sites so as to improve drug-placebo discrimination in antidepressant clinical trials. The method appears to increase the signal in situations in which the test drug is better than placebo. However, confirmation of its impact on the rate of false-positive results is needed before the method can be used prospectively.Clinical Pharmacology & Therapeutics (2010) 88 5, 592-594. doi: 10.1038/clpt.2010.222
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ABSTRACT: The gold standard for determining the efficacy of biomedical therapies is the detection of a significant difference between the therapeutic effects of an active pharmacological agent or procedure and a matched inert placebo in a randomized controlled trial. Detecting this difference has become a challenge for medicine, especially for outcomes that are based on patient self-rated scales. Yet factors that contribute to placebo responses have received scant attention. In this issue of Science Translational Medicine, Bingel et al. report on an example of how noninvasive whole-brain imaging contributes to our understanding of brain-based placebo effects. Here we highlight ways in which neuroimaging is catalyzing a revolution in society's perspective of placebo effects by providing a compelling visualization of how brain activities that reflect a person's thoughts, feelings, and past experiences can enhance or antagonize his or her response to a medical treatment.
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