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.