A Comparison of Variable Selection Approaches for Dynamic Treatment Regimes

Abstract

In estimating optimal adaptive treatment strategies, the tailor treatment variables used for patient profiles are typically hand-picked by experts. However these variables may not yield an estimated optimal dynamic regime that is close to the optimal regime which uses all variables. The question of selecting tailoring variables has not yet been answered satisfactorily, though promising new approaches have been proposed. We compare the use of reducts--a variable selection tool from computer sciences--to the S-score criterion proposed by Gunter and colleagues in 2007 for suggesting collections of useful variables for treatment regime tailoring. Although the reducts-based approach promised several advantages such as the ability to account for correlation among tailoring variables, it proved to have several undesirable properties. The S-score performed better, though it too exhibited some disappointing qualities.