A Random Effects Model for Binary Data from Crossover Clinical Trials

Abstract

The use of a random effects model for binary data in the interpretation of crossover studies is described. The model incorporates normally distributed subject effects, common to all responses from the same subject, into the linear part of the logistic regression model. The case of two treatments and two periods is considered, although extensions of the methodology to more general cases are possible. The paper describes how the model can be fitted and how the results can be interpreted. It is shown how data from subjects who miss the second period of treatment can be included in the analysis. Implications of the model on sample size calculations are studied, and a table to aid such calculations is provided. The methodology is illustrated with data from a recent pharmarceutical study of inhalation devices.