Efficient Surrogate Assisted Inference for Patient-Reported Outcome with Complex Missing Mechanism

• Activity Number: 235 - Recent Advancements in Nonparametric and Semiparametric Methodologies and Their Applications
• Type: Topic Contributed
• Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Muxuan Liang* and Jaeyoung Park and Xiang Zhong and Yingqi Zhao
• Companies: University of Florida and University of Florida and University of Florida and Fred Hutchinson Cancer Research Center
• Keywords: Missing outcomes; High missing rate; Semiparametric inference; Dimension reduction; Risk prediction

Abstract

Patient-reported outcome (PRO) measures are increasingly collected as a means of measuring healthcare quality and value. However, PRO measures often suffer from a high missing rate, and the missingness may depend on many patient factors. Under such a complex missing mechanism, statistical inference of the parameters in a model for predicting PRO measures is challenging. In this work, we propose to use an informative surrogate that can lead to a flexible imputation model lying in a low-dimensional subspace to efficiently infer the parameters of interest. To remove the bias due to the flexible imputation model, we identify a class of weighting functions as alternatives to the traditional propensity score and estimate a low-dimensional weighting function within the identified function class. Based on the estimated low-dimensional weighting function, we construct a one-step debiased estimator without using any information of the true missing propensity. We establish the asymptotic normality of the one-step debiased estimator. Simulation and an application to real-world data demonstrate the superiority of the proposed method.