Multistage Estimators for Missing Covariates and Incomplete Outcomes

TL;DR

This paper introduces a comprehensive framework for handling complex missing data problems in covariates and outcomes, combining inverse probability weighting, regression adjustment, and robustness, with applications to survival analysis, causal inference, and real data.

Contribution

It presents a novel multiply-robust approach for missing covariates and outcomes, extending classical models with new theoretical and practical tools.

Findings

Effective in simulations and real Alzheimer's data

Provides asymptotic and identifying theories

Improves estimation accuracy in missing data scenarios

Abstract

We study problems with multiple missing covariates and partially observed responses. We develop a new framework to handle complex missing covariate scenarios via inverse probability weighting, regression adjustment, and a multiply-robust procedure. We apply our framework to three classical problems: the Cox model from survival analysis, missing response, and binary treatment from causal inference. We also discuss how to handle missing covariates in these scenarios, and develop associated identifying theories and asymptotic theories. We apply our procedure to simulations and an Alzheimer's disease dataset and obtain meaningful results.