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In precision medicine, Dynamic Treatment Regimes (DTRs) are treatmentprotocols that adapt over time in response to a patient's observedcharacteristics. A DTR is a set of decision functions that takes an individualpatient's information as arguments and outputs an action to be taken. Buildingon observed data, the aim is to identify the DTR that optimizes expectedpatient outcomes. Multiple methods have been proposed for optimal DTRestimation with continuous outcomes. However, optimal DTR estimation withbinary outcomes is more complicated and has received comparatively littleattention. Solving a system of weighted generalized estimating equations, wepropose a new balancing weight criterion to overcome the misspecification ofgeneralized linear models' nuisance components. We construct binarypseudo-outcomes, and develop a doubly-robust and easy-to-use method to estimatean optimal DTR with binary outcomes. We also outline the underlying theory,which relies on the balancing property of the weights; provide simulationstudies that verify the double-robustness of our method; and illustrate themethod in studying the effects of e-cigarette usage on smoking cessation, usingobservational data from the Population Assessment of Tobacco and Health (PATH)study.

Doubly-Robust Dynamic Treatment Regimen Estimation for Binary Outcomes