B0378
Title: Estimation and evaluation of individualized treatment rules following multiple imputation
Authors: Jenny Shen - University of Pennsylvania (United States)
Rebecca Hubbard - University of Pennsylvania (United States)
Kristin Linn - University of Pennsylvania (United States) [presenting]
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. The motivation is from the Social Incentives to Encourage Physical Activity and Understand Predictors (STEP UP) study, where participants were randomized interventions designed to increase physical activity. Study participants were given wearable devices which were used to record daily step counts as a measure of physical activity. Many participants were missing at least one daily step count during the study period. Two frameworks for the estimation and evaluation of an optimal treatment decision rule are proposed following multiple imputations and comparing the performance of the frameworks using simulated data. The methods are applied to the STEP UP data to determine whether a personalized intervention strategy might be expected to increase physical activity more than the single intervention that had the largest estimated average treatment effect.
