A New Population-Enrichment Strategy to Improve Efficiency of Placebo-Controlled Clinical Trials of Antidepressant Drugs
ABSTRACT 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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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.2010.222Clinical Pharmacology & Therapeutics 11/2010; 88(5):592-4. DOI:10.1038/clpt.2010.222 · 7.90 Impact Factor
- Clinical Pharmacology & Therapeutics 02/2011; 89(4):485; author reply 486-7. DOI:10.1038/clpt.2010.300 · 7.90 Impact Factor
- Clinical Pharmacology & Therapeutics 02/2011; 89(4):485; author reply 486-7. DOI:10.1038/clpt.2010.275 · 7.90 Impact Factor