A bias-reduced generalized estimating equation approach for proportional odds models with small-sample longitudinal ordinal data

• Published in: BMC medical research methodology Vol. 24; no. 1; pp. 140 - 13
• Main Authors: Tada, Yukio, Sato, Tosiya
• Format: Journal Article
• Language: English
• Published: London BioMed Central 28.06.2024; Springer Nature B.V; BMC
• Subjects: Algorithms; Associations; Bias; Bias reduction; Categorical data; Clinical trials; Computer Simulation; Data Interpretation, Statistical; Estimates; Firth’s adjustment; Generalized linear models; Health Sciences; Humans; Likelihood Functions; Longitudinal Studies; Marginal model; Medical research; Medicine; Medicine & Public Health; Models, Statistical; Penalization; Research methodology; Sample Size; Simulation; Statistical Theory and Methods; Statistics for Life Sciences; Theory of Medicine/Bioethics; Marginal model; Bias reduction; Categorical data; Firth’s adjustment; Penalization
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-024-02259-6

Background Longitudinal ordinal data are commonly analyzed using a marginal proportional odds model for relating ordinal outcomes to covariates in the biomedical and health sciences. The generalized estimating equation (GEE) consistently estimates the regression parameters of marginal models even if the working covariance structure is misspecified. For small-sample longitudinal binary data, recent studies have shown that the bias of regression parameters may result from the GEE and have addressed the issue by applying Firth’s adjustment for the likelihood score equation to the GEE as if generalized estimating functions were likelihood score functions. In this manuscript, for the proportional odds model for longitudinal ordinal data, the small-sample properties of the GEE were investigated, and a bias-reduced GEE (BR-GEE) was derived. Methods By applying the adjusted function originally derived for the likelihood score function of the proportional odds model to the GEE, we produced the BR-GEE. We investigated the small-sample properties of both GEE and BR-GEE through simulation and applied them to a clinical study dataset. Results In simulation studies, the BR-GEE had a bias closer to zero, smaller root mean square error than the GEE with coverage probability of confidence interval near or above the nominal level. The simulation also showed that BR-GEE maintained a type I error rate near or below the nominal level. Conclusions For the analysis of longitudinal ordinal data involving a small number of subjects, the BR-GEE is advantageous for obtaining estimates of the regression parameters of marginal proportional odds models.