A note on prognostic accuracy evaluation of regression models applied to longitudinal autocorrelated binary data (Epidemiology, Biostatistics, and Public Health, 2022)
Background: Focus of this work was on evaluating the prognostic accuracy of two approaches for modelling binary longitudinal outcomes, a Generalized Estimating Equation (GEE) and a likelihood based method, Marginalized Transition Model (MTM), in which a transition model is combined with a marginal generalized linear model describing the average response as a function of measured predictors.Methods: A retrospective study on cardiovascular patients and a prospective study on sciatic pain were used to evaluate discrimination by computing the Area Under the Receiver-Operating-Characteristics curve, (AUC), the Integrated Discrimination Improvement (IDI) and the Net Reclassification Improvement (NRI) at different time occasions. Calibration was also evaluated. A simulation study was run in order to compare model’s performance in a context of a perfect knowledge of the data generating mechanism.Results: Similar regression coefficients estimates and comparable calibration were obtained; an higher discrimination level for MTM was observed. No significant differences in calibration and MSE (Mean Square Error) emerged in the simulation study, that instead confirmed the MTM higher discrimination level.Conclusions: The choice of the regression approach should depend on the scientific question being addressed, i.e. if the overall population-average and calibration or the subject-specific patterns and discrimination are the objectives of interest, and some recently proposed discrimination indices are useful in evaluating predictive accuracy also in a context of longitudinal studies
Keywords: Area Under the Receiver-Operating-Characteristics curve (AUC); Net Reclassification Improvement (NRI); Integrated Discrimination Improvement (IDI); Generalized Estimating Equation (GEE); Marginalized Transition Model (MTM); longitudinal binary data