Weighted generalized estimating equations and unified estimation for longitudinal data with nonmonotone missing data patterns

Abstract

Missing data are a major complication in longitudinal data analysis. Weighted generalized estimating equations (WGEEs, Robins et al, J Am Stat Assoc 1995;90:106-121) were developed to deal with missing response data. They have been extended for data with both missing responses and missing covariates (Chen et al, J Am Stat Assoc 2010;105:336-353). However, it may introduce more variability in dealing with the correlation structure of the responses. We propose new WGEEs for missing at random data where both response and (time-dependent) covariates may have values missing in nonmonotone missing data patterns. We also explain how to improve the estimation efficiency of WGEEs using a unified approach (Zhao and Liu, AStA Adv Stat Anal 2021;105(1):87-101). The proposed unified estimator is consistent and more efficient than the regular WGEE estimator. It is computationally simple and can be directly implemented in standard software. Simulation studies for both continuous response and binary response data are provided to examine the performance of the proposed estimators. A clinical trial example investigating the quality of life of women with early-stage breast cancer and the associated factors is analyzed.