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; MTM higher discrimination level was confirmed.
ConclusionS: The choice of the regression approach should depend on the scientific question being
addressed: whether the overall population-average and calibration are the objectives of interest, or
the subject-specific patterns and discrimination. Moreover, some recently proposed discrimination
indices are useful in evaluating predictive accuracy also in a context of longitudinal studies.
