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Sequential causal inference: Application to randomized trials of adaptive treatment strategies by Ree Dawson; Philip W. Lavori is a Mathematics article available to read on EtoBox.

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## Abstract Clinical trials that randomize subjects to decision algorithms, which adapt treatments over time according to individual response, have gained considerable interest as investigators seek designs that directly inform clinical decision making. We consider designs in which subjects are randomized sequentially at decision points, among adaptive treatment options under evaluation. We present a sequential method to estimate the comparative effects of the randomized adaptive treatments, which are formalized as adaptive treatment strategies. Our causal estimators are derived using Bayesian predictive inference. We use analytical and empirical calculations to compare the predictive estimators to (i) the ‘standard’ approach that allocates the sequentially obtained data to separate strategy‐specific groups as would arise from randomizing subjects at baseline; (ii) the semi‐parametric approach of marginal mean models that, under appropriate experimental conditions, provides the same sequential estimator of causal differences as the proposed approach. Simulation studies demonstrate that sequential causal inference offers substantial efficiency gains over the standard approach to com

Who reads Sequential causal inference: Application to randomized trials of adaptive treatment strategies?

Author

Ree Dawson; Philip W. Lavori

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2007

Language

EN

Field

Mathematics (Physical Sciences)