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Statistics Seminar

Estimation and Evaluation of Individualized Treatment Rules Following Multiple Imputation by Kristin Linn

Event Details

Wednesday, September 14, 2022
4-5 p.m.

Date: September 14, 2022

Speaker: Kristin Linn


Title: Estimation and Evaluation of Individualized Treatment Rules Following Multiple Imputation

Abstract: Data-driven optimal treatment strategies promise to benefit patients, care providers, and other stakeholders by improving clinical outcomes and lowering healthcare costs. A treatment decision rule is a function that inputs patient-level information and outputs a recommended treatment. An important focus of precision medicine is to develop optimal treatment decision rules that maximize a population-level distributional summary such as the expected value of a clinical outcome. However, guidance for estimating and evaluating optimal treatment decision rules in the presence of missing data is fairly limited. Our work is motivated by the Social Incentives to Encourage Physical Activity and Understand Predictors (STEP UP) study. In this trial, participants were randomized to a control arm or one of multiple interventions designed to increase physical activity. Daily step counts obtained from wearable devices were used to quantify physical activity. Many participants had at least one day with a missing step count during the study period, and the missingness pattern within individuals was often non-monotone. We propose two frameworks for estimation and evaluation of an optimal treatment decision rule following multiple imputation and compare their performance using simulated data. We then apply our methods to the STEP UP data to determine whether a personalized intervention strategy is expected to increase physical activity more than the single intervention with the largest estimated average treatment effect.