Variable selection in regression‐based estimation of dynamic treatment regimes-Reference-Cited by-同舟云学术

Variable selection in regression‐based estimation of dynamic treatment regimes

Published:2021-12-18 Issue: Volume: Page:
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Bian Zeyu¹^ORCID,Moodie Erica E. M.¹^ORCID,Shortreed Susan M.²³^ORCID,Bhatnagar Sahir¹⁴^ORCID

Affiliation:

1. Department of Epidemiology, Biostatistics and Occupational Health McGill University Montreal Quebec Canada

2. Kaiser Permanente Washington Health Research Institute Seattle Washington USA

3. Department of Biostatistics University of Washington Seattle Washington USA

4. Department of Diagnostic Radiology McGill University Montreal Quebec Canada

Funder

National Institute of Mental Health

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13608

Reference38 articles.

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4. The Dantzig selector: Statistical estimation when p is much larger than n

5. Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m ‐Out‐of‐ n Bootstrap Scheme

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1. Statistical Reinforcement Learning and Dynamic Treatment Regimes;ICSA Book Series in Statistics;2024

2. A reluctant additive model framework for interpretable nonlinear individualized treatment rules;The Annals of Applied Statistics;2023-12-01

3. Variable selection for individualised treatment rules with discrete outcomes;Journal of the Royal Statistical Society Series C: Applied Statistics;2023-11-02

4. Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms;Biostatistics;2023-08-31