Responder analysis without dichotomization

In clinical trials, it is common practice to categorize subjects as responders and non-responders on the basis of one or more clinical measurements under pre-specified rules. Such a responder analysis is often criticized for the loss of information in dichotomizing one or more continuous or ordinal variables. It is worth noting that a responder analysis can be performed without dichotomization, because the proportion of responders for each treatment can be derived from a model for the original clinical variables (used to define a responder) and estimated by substituting maximum likelihood estimators of model parameters. This model-based approach can be considerably more efficient and more effective for dealing with missing data than the usual approach based on dichotomization. For parameter estimation, the model-based approach generally requires correct specification of the model for the original variables. However, under the sharp null hypothesis, the model-based approach remains unbiased for estimating the treatment difference even if the model is misspecified. We elaborate on these points and illustrate them with a series of simulation studies mimicking a study of Parkinson’s disease, which involves longitudinal continuous data in the definition of a responder.