Placebo Response as a Latent Characteristic: Application to Analysis of Sequential Parallel Comparison Design Studies

Denis Rybin, Robert Lew, Michael J. Pencina, Maurizio Fava and Gheorghe Doros

Journal of the American Statistical Association, 2018, vol. 113, issue 524, 1411-1430

Abstract: In clinical trials, placebo response can affect the inference about efficacy of the studied treatment. It is important to have a robust way to classify trial subjects with respect to their response to placebo. Simple, criterion-based classification may lead to classification error and bias the inference. The uncertainty about placebo response characteristic has to be factored into the treatment effect estimation. We propose a novel approach that views the placebo response as a latent characteristic and the study sample as an unlabeled mixture of “placebo responders” and “placebo nonresponders.” The likelihood-based methodology is used to estimate the treatment effect corrected for placebo response as defined within sequential parallel comparison design.

Date: 2018
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DOI: 10.1080/01621459.2017.1375930

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