Analyzing sequentially randomized trials based on causal effect models for realistic individualized treatment rules

In this paper, we argue that casual effect models for realistic individualized treatment rules represent an attractive tool for analyzing sequentially randomized trials. Unlike a number of methods proposed previously, this approach does not rely on the assumption that intermediate outcomes are discrete or that Models for the distributions of these intermediate outcomes given the observed past are correctly specified. In addition, it generalizes the methodology for performing pairwise comparisons between individualized treatment rules by allowing the user to posit a marginal structural Model for all candidate treatment rules simultaneously. This is particularly Useful if the number Of such rules is large, in which case all approach based oil individual pairwise comparisons would be likely to stiffer front too much sampling variability to provide in informative answer. In addition, such causal effect models represent an interesting alternative to methods previously proposed for selecting ail optimal individualized treatment rule in that they immediately give the User a sense of how the optimal outcome is estimated to change ill the neighborhood of the identified optimum. We discuss an inverse-of-probability-of-treatment-weighted (IPTW) estimator for these causal effect models, which is straightforward to implement using standard statistical software, and develop) ail approach for constructing valid asymptotic confidence intervals based oil the influence curve of this estimator. The methodology is illustrated in two simulations studies that are intended to mimic an HIV/AIDS trial. Copyright (c) 2008 John Wiley & Sons, Ltd.