Modeling the Association Structure in Doubly Robust GEE for Longitudinal Ordinal Missing Data

TL;DR

This paper extends the doubly robust GEE method for longitudinal ordinal data by modeling the association structure with correlation coefficients or local odds ratios, improving efficiency especially in small samples.

Contribution

It introduces a novel extension of DRGEE that incorporates structured association modeling, enhancing performance over standard independent correlation assumptions.

Findings

Local odds ratio parametrization outperforms correlation coefficient in simulations.

Structured association modeling improves efficiency in small samples.

Method successfully applied to Rheumatic Mitral Stenosis data.

Abstract

Generalized Estimation Equations (GEE) are a well-known method for the analysis of categorical longitudinal responses. GEE method has computational simplicity and population parameter interpretation. In the presence of missing data it is only valid under the strong assumption of missing completely at random. A doubly robust estimator (DRGEE) for correlated ordinal longitudinal data is a nice approach for handling intermittently missing response and covariate under the MAR mechanism. Independent working correlation is the standard way in DRGEE. However, when covariate is not time stationary, efficiency can be gained using a structured association. The goal of this paper is to extend the DRGEE estimator to allow modeling the association structure by means of either the correlation coefficient or local odds ratio. Simulation results revealed better performance of the local odds ratio parametrization, specially for small samples. The method is applied to a data set related to Rheumatic Mitral Stenosis.