On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies

• Activity Number: 483 - Statistical Approaches in Precision Medicine
• Type: Contributed
• Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Biometrics Section
• Author(s): Hengrui Cai* and Wenbin Lu and Rui Song
• Companies: Department of Statistics, North Carolina State University and North Carolina State University and NC State University
• Keywords: Augmented inverse propensity weighted estimation; Hypothesis testing; Individualized treatment rule; Optimal decision making; Sample size calculation; Value function

Abstract

In the current era of personalized recommendation, one major interest is to develop an optimal individualized decision rule that assigns individuals with the best treatment option according to their covariates. Estimation of optimal decision rules (ODR) has been extensively investigated recently, however, at present, no testing procedure is proposed to verify whether these ODRs are significantly better than the naive decision rule that always assigning individuals to a fixed treatment option. In this paper, we propose a testing procedure for detecting the existence of an ODR that is better than the naive decision rule under the randomized trials. We construct the proposed test based on the difference of estimated value functions using the augmented inverse probability weighted method. The asymptotic distributions of the proposed test statistic under the null and local alternative hypotheses are established. Based on the established asymptotic distributions, we further develop a sample size calculation formula for testing the existence of an ODR in designing A/B tests.