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

Abstract

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.

Keywords:

• Ordinal categorical data
• longitudinal data
• generalized estimating equations
• binary dynamic logit for correlated ordinal
• repeated measures

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 We have also tried to compare the results for different values of the parameters using 1,000 simulations, and the conclusions are consistent.