Year: 2023
Type: article
Abstract: In recent sequential multiple assignment randomized trials, outcomes were assessed multiple times to evaluate longer-term impacts of the dynamic treatment regimes (DTRs). Q-learning requires a scalar response to identify the optimal DTR. Inverse probability weighting may be used to estimate the optimal outcome trajectory, but it is inefficient, susceptible to model mis-specification, and unable to characterize how treatment effects manifest over time. We propose modified Q-learning with generalized estimating equations to address these limitations and apply it to the M-bridge trial, which evaluates adaptive interventions to prevent problematic drinking among college freshmen. Simulation studies demonstrate our proposed method improves efficiency and robustness.
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: University of Minnesota Clinical and TranslationalmScience Institute (UMN CTSI), SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment, G555927477, QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data , An Adaptive Preventive Intervention to Optimize the Transition from Universal to Indicated Resources for College Student Alcohol Use, Evaluation of an Explicit Approach to Teach Grammatical Forms to Children with Language Impairment, Cancer Center Support Grant
OpenAlex ID: W4377823277