Variable selection for optimal treatment decision.

TitleVariable selection for optimal treatment decision.
Publication TypeJournal Article
Year of Publication2013
AuthorsLu, Wenbin, Hao Helen Zhang, and Donglin Zeng
JournalStat Methods Med Res
Date Published2013 Oct
KeywordsDecision Making, Female, Humans, Male, Models, Theoretical, Precision Medicine

In decision-making on optimal treatment strategies, it is of great importance to identify variables that are involved in the decision rule, i.e. those interacting with the treatment. Effective variable selection helps to improve the prediction accuracy and enhance the interpretability of the decision rule. We propose a new penalized regression framework which can simultaneously estimate the optimal treatment strategy and identify important variables. The advantages of the new approach include: (i) it does not require the estimation of the baseline mean function of the response, which greatly improves the robustness of the estimator; (ii) the convenient loss-based framework makes it easier to adopt shrinkage methods for variable selection, which greatly facilitates implementation and statistical inferences for the estimator. The new procedure can be easily implemented by existing state-of-art software packages like LARS. Theoretical properties of the new estimator are studied. Its empirical performance is evaluated using simulation studies and further illustrated with an application to an AIDS clinical trial.

Alternate JournalStat Methods Med Res
Original PublicationVariable selection for optimal treatment decision.
PubMed ID22116341
PubMed Central IDPMC3303960
Grant ListR01 CA082659 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
R01 CA140632 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 CA140632-02 / CA / NCI NIH HHS / United States
P30 AI050410 / AI / NIAID NIH HHS / United States