ArticleBiometrics2023
Variable selection in regression-based estimation of dynamic treatment regimes.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Variable selection for estimating optimal treatment regimes with multiple treatments.Scientific reports · 2026Article
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Simultaneous Feature Selection for Optimal Dynamic Treatment Regimens.Statistics in medicine · 2025Article
- Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes.Biometrics · 2025Article
- Dynamic Treatment Regimes on Dyadic Networks.Statistics in medicine · 2024Article
- Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms.Biostatistics (Oxford, England) · 2024Article
- Variable selection for individualised treatment rules with discrete outcomes.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- A RELUCTANT ADDITIVE MODEL FRAMEWORK FOR INTERPRETABLE NONLINEAR INDIVIDUALIZED TREATMENT RULES.The annals of applied statistics · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Dynamic treatment regimes (DTRs) consist of a sequence of decision rules, one per stage of intervention, that aim to recommend effective treatments for individual patients according to patient information history. DTRs can be estimated from models which include interactions between treatment and a (typically small) number of covariates which are often chosen a priori. However, with increasingly large and complex data being collected, it can be difficult to know which prognostic factors might be relevant in the treatment rule. Therefore, a more data-driven approach to select these covariates might improve the estimated decision rules and simplify models to make them easier to interpret. We propose a variable selection method for DTR estimation using penalized dynamic weighted least squares. Our method has the strong heredity property, that is, an interaction term can be included in the model only if the corresponding main terms have also been selected. We show our method has both the double robustness property and the oracle property theoretically; and the newly proposed method compares favorably with other variable selection approaches in numerical studies. We further illustrate the proposed method on data from the Sequenced Treatment Alternatives to Relieve Depression study.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.