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A dynamic treatment regime (DTR) is a sequence of decision rules, each of which recommends a treatment based on a patient's past and current health status. Sequential, multiple assignment, randomized trials (SMARTs) are multi-stage trial designs that yield data specifically for building effective DTRs. Modeling the marginal mean trajectories of a repeated-measures outcome arising from a SMART presents challenges, because traditional longitudinal models used for randomized clinical trials do not take into account the unique design features of SMART. We discuss modeling considerations for various forms of SMART designs, emphasizing the importance of considering the timing of repeated measures in relation to the treatment stages in a SMART. For illustration, we use data from three SMART case studies with increasing level of complexity, in autism, child attention deficit hyperactivity disorder, and adult alcoholism. In all three SMARTs, we illustrate how to accommodate the design features along with the timing of the repeated measures when comparing DTRs based on mean trajectories of the repeated-measures outcome. [ABSTRACT FROM AUTHOR]

Tom Ten Have made many contributions to causal inference and biostatistics before his untimely death. This paper reviews Tom's contributions and discusses potential related future research directions. We focus on Tom's contributions to longitudinal/repeated measures categorical data analysis and particularly his contributions to causal inference. Tom's work on causal inference was primarily in the areas of estimating the effect of receiving treatment in randomized trials with nonadherence and mediation analysis. A related area to mediation analysis he was working on at the time of his death was posttreatment effect modification with applications to designing adaptive treatment strategies. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In many clinical studies, the disease of interest is multifaceted, and multiple outcomes are needed to adequately capture information about the characteristics of the disease or its severity. In the analysis of such diseases, it is often difficult to determine what constitutes improvement because of the multivariate nature of the outcome. Furthermore, when the disease of interest has an unknown etiology and/or is primarily a symptom-defined syndrome, there is potential for the disease population to have distinct subgroups. Identification of population subgroups is of interest as it may assist clinicians in providing appropriate treatment or in developing accurate prognoses. We propose multivariate growth curve latent class models that group subjects on the basis of multiple symptoms measured repeatedly over time. These groups or latent classes are defined by distinctive longitudinal profiles of a latent variable, which is used to summarize the multivariate outcomes at each point. The mean growth curve for the latent variable in each class defines the features of the class. We develop this model for any combination of continuous, binary, ordinal, or count outcomes within a Bayesian hierarchical framework. We use simulation studies to validate the estimation procedures. We apply our model to data from a randomized clinical trial evaluating the efficacy of Bacillus Calmette-Guerin in treating symptoms of interstitial cystitis where we are able to identify a class of subjects for whom treatment is effective. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]