Binary Dynamic Logit for Correlated Ordinal: estimation, application and simulation

• Published in: Journal of applied statistics Vol. 49; no. 10; pp. 2657 - 2673
• Main Authors: Li, Yingzi, Liu, Huinan, Dasgupta, Nairanjana
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 27.07.2022; Taylor & Francis Ltd
• Subjects: Application Notes; binary dynamic logit for correlated ordinal; Correlation; Estimation; generalized estimating equations; Logit models; longitudinal data; Ordinal categorical data; Other; Performance evaluation; repeated measures; Statistical methods; Within-subjects design
• ISSN: 0266-4763; 1360-0532; 1360-0532
• DOI: 10.1080/02664763.2021.1906849

We evaluate the estimation performance of the Binary Dynamic Logit model for correlated ordinal variables (BDLCO model), and compare it to GEE and Ordinal Logistic Regression performance in terms of bias and Mean Absolute Percentage Error (MAPE) via Monte Carlo simulation. Our results indicate that when the proportional-odds assumption does not hold, the proposed BDLCO method is superior to existing models in estimating correlated ordinal data. Moreover, this method is flexible in terms of modeling dependence and allows unequal slopes for each category, and can be used to estimate an apple bloom data set where the proportional-odds assumption is violated. We also provide a function in R to implement BDLCO.