Efficient surrogate-assisted inference for patient-reported outcome measures with complex missing mechanism

TL;DR

This paper introduces an efficient method for inferring patient-reported outcome measures with complex missing data by using surrogate variables and low-dimensional weighting functions to reduce bias and improve estimation accuracy.

Contribution

The paper proposes a novel surrogate-assisted inference approach that leverages low-dimensional weighting functions to handle complex missing mechanisms in PRO data.

Findings

The method achieves asymptotic normality of the estimator.

Simulation studies show improved bias reduction.

Application to real data demonstrates practical effectiveness.

Abstract

Patient-reported outcome (PRO) measures are increasingly collected as a means of measuring healthcare quality and value. The capability to predict such measures enables patient-provider shared decision making and the delivery of patient-centered care. However, due to their voluntary nature, 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 prediction models for PRO measures is challenging, especially when flexible imputation models such as machine learning or nonparametric methods are used. Specifically, the slow convergence rate of the flexible imputation model may lead to non-negligible bias, and the traditional missing propensity, capable of removing such a bias, is hard to estimate due to the complex missing mechanism. To efficiently infer the parameters of interest, we propose to use an informative surrogate that can lead to a flexible imputation model lying in a low-dimensional subspace. 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 the low-dimensional one 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.