Doubly-Robust Dynamic Treatment Regimen Estimation for Binary Outcomes

Jiang, Cong; Wallace, Michael B.; Thompson, Mary

doi:10.48550/arxiv.2203.08269

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…In practice, we illustrate the method in an analysis of e-cigarette usage and smoking cessation, using observational data from the PATH study. We have summarized this chapter as a research paper, Jiang et al [2022a], which has been posted to the arXiv.…”

Section: Contributions and Organization Of Thesismentioning

confidence: 99%

Dynamic treatment regimes with interference

2022

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Precision medicine describes health care where patient‐level data are used to inform treatment decisions. Within this framework, dynamic treatment regimes (DTRs) are sequences of decision rules that take individual patient information as input data and then output treatment recommendations. DTR estimation from observational data typically relies on the assumption of no interference: i.e., the outcome of one individual is unaffected by the treatment assignment of others. However, in many social network contexts, such as friendship or family networks, and for many health concerns, such as infectious diseases, this assumption is questionable. We investigate the DTR estimation method of dynamic weighted ordinary least squares (dWOLS), which boasts of easy implementation and the so‐called double‐robustness property, but relies on the assumption of no interference. We define a network propensity function and build on it to establish an implementation of dWOLS that remains doubly robust under interference associated with network links. The method's properties are demonstrated via simulation and applied to data from the Population Assessment of Tobacco and Health (PATH) study to investigate cigarette dependence within two‐person household networks.

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Section: Contributions and Organization Of Thesismentioning

confidence: 99%

Dynamic treatment regimes with interference

2022

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“…The rationale for using a backward recursive algorithm is the following. Backward recursion is a dynamic programming method used for learning optimal DTRs (Murphy, 2003) that addresses two fundamental challenges in sequential decision-making: (1) We must account for both prognostic and delayed effects of past decisions and the current decision (Jiang et al, 2022). ( 2) Time-varying covariates can be both confounders and mediators of treatment effects depending on their time of measurement in relation to the treatment decision.…”

Section: Overviewmentioning

confidence: 99%

Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine

Blümlein¹,

Persson²,

Feuerriegel³

2022

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Dynamic treatment regimes (DTRs) are used in medicine to tailor sequential treatment decisions to patients by considering patient heterogeneity. Common methods for learning optimal DTRs, however, have shortcomings: they are typically based on outcome prediction and not treatment effect estimation, or they use linear models that are restrictive for patient data from modern electronic health records. To address these shortcomings, we develop two novel methods for learning optimal DTRs that effectively handle complex patient data. We call our methods DTR-CT and DTR-CF. Our methods are based on a datadriven estimation of heterogeneous treatment effects using causal tree methods, specifically causal trees and causal forests, that learn non-linear relationships, control for time-varying confounding, are doubly robust, and explainable. To the best of our knowledge, our paper is the first that adapts causal tree methods for learning optimal DTRs. We evaluate our proposed methods using synthetic data and then apply them to real-world data from intensive care units. Our methods outperform state-of-the-art baselines in terms of cumulative regret and percentage of optimal decisions by a considerable margin. Our work improves treatment recommendations from electronic health record and is thus of direct relevance for personalized medicine.

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Doubly-Robust Dynamic Treatment Regimen Estimation for Binary Outcomes

Cited by 2 publications

References 23 publications

Dynamic treatment regimes with interference

Dynamic treatment regimes with interference

Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine

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