期刊
出版社
OXFORD UNIV PRESS
DOI: 10.1093/jrsssc/qlad096
关键词
double robustness; penalisation; precision medicine; variable selection; weighted generalised linear model
An individualised treatment rule (ITR) is a decision rule that improves individuals' health outcomes by recommending treatments based on subject-specific information. It is important to select variables to improve the treatment rule and exclude irrelevant variables to increase efficiency and simplify the rule.
An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement. Thus, selecting variables to improve the treatment rule is crucial. We propose a doubly robust variable selection method for ITRs, and show that it compares favourably with competing approaches. We illustrate the proposed method on data from an adaptive, web-based stress management tool.
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