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

Statistics in Medicine
Oliver Bembom, Mark J van der Laan

Abstract

In this paper, we argue that causal 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 an approach based on individual pairwise comparisons would be likely to suffer from too much sampling variability to provide an informative answer. In addition, such causal effect models represent an interesting alternative to methods previously proposed for selecting an optimal individualized treatment rule in that they immediately give the user a sense of how the optimal outcome is estimated to change in the neighborhood of the identified optimum. We discuss an inverse-probability-of-treatment-weighted (IPTW) es...Continue Reading

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Citations

Jul 16, 2010·Clinical Trials : Journal of the Society for Clinical Trials·Ree Dawson, Philip W Lavori
Sep 30, 2014·Translational Behavioral Medicine·Daniel AlmirallSusan A Murphy
Apr 1, 2009·Journal of the Royal Statistical Society. Series A, (Statistics in Society)·Michael RosenblumNancy Padian
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Feb 19, 2014·Statistics in Medicine·Romain NeugebauerMark J van der Laan
Apr 7, 2020·Biometrics·Q ClaironC J Taylor
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Feb 24, 2020·Critical Care : the Official Journal of the Critical Care Forum·Zach ShahnLi-Wei H Lehman
Mar 27, 2021·Statistics in Biosciences·David BenkeserMichael Hudgens

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