The placebo response can affect inference in analysis of data from clinical trials. It can bias the estimate of the treatment effect, jeopardize the effort of all involved in a clinical trial and ultimately deprive patients of potentially efficacious treatment. The Sequential Parallel Comparison Design (SPCD) is one of the novel approaches addressing placebo response in clinical trials. The analysis of SPCD clinical trial data typically involves classification of subjects as ‘placebo responders’ or ‘placebo non-responders’. This classification is done using a specific criterion and placebo response is treated as a measurable characteristic. However, the use of criterion may lead to subject misclassification due to measurement error or incorrect criterion selection. Subsequently, misclassification can directly affect SPCD treatment effect estimate. We propose to view placebo response as an unknown random characteristic that can be estimated based on information collected during the trial. Two strategies are presented here. First strategy is to model placebo response using criterion classification as a starting point or the observed data, and to include the placebo response estimate into the treatment effect estimation. Second strategy is to jointly model latent placebo response and the observed data, and estimate treatment effect from the joint model. We evaluate both strategies on a wide range of simulated data scenarios in terms of type I error control, mean squared error and power. We then evaluate the strategies in presence of missing data and propose a method for missing data imputation under the non-informative missingness assumption. The data from a recent SPCD clinical trial is used to compare results of the proposed methods with reported results of the trial.2018-01-01T00:00:00
