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Updated: Jun 30, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Variable selection for individualised treatment rules with discrete outcomes
Zeyu Bian1,2, Erica E M Moodie1, Susan M Shortreed3,4
1Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec H3A 0G4, Canada.
Abstract:
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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