title: Responder analysis without dichotomization.
abstract: 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.
authors.affiliation: a Department of Statistics , University of California , Riverside , California , USA.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: b Division of Biostatistics , Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration , Silver Spring , Maryland , USA.
authors.forename: Jianxiong
authors.initials: J
authors.lastname: Chu
authors.affiliation: c United States Department of Defense , Fort Meade , Maryland , USA.
authors.forename: Dewi
authors.initials: D
authors.lastname: Rahardja
authors.affiliation: d Department of Biostatistics , St. Jude Children's Research Hospital , Memphis , Tennessee , USA.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: d Department of Biostatistics , St. Jude Children's Research Hospital , Memphis , Tennessee , USA.
authors.forename: Li
authors.initials: L
authors.lastname: Tang
mesh_terms: D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D010300:Parkinson Disease / Q000628:therapy; D011336:Probability; D016896:Treatment Outcome
keywords: Clinical trial; delta method; efficiency; information bound; missing data; robustness