A note on prognostic accuracy evaluation of regression models applied to longitudinal autocorrelated binary data

Barbati Giulia, Farcomeni Alessio, Pasqualetti Patrizio, Sinagra Gianfranco, Bovenzi Massimo

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalEpidemiology Biostatistics and Public Health
Volume11
Issue number4
DOIs
Publication statusPublished - 2014

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Keywords

  • Area Under the Receiver-Operating-Characteristics curve (AUC)
  • Generalized Estimating Equation (GEE)
  • Integrated Discrimination Improvement (IDI)
  • Longitudinal binary data
  • Marginalized Transition Model (MTM)
  • Net Reclassification Improvement (NRI)

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Public Health, Environmental and Occupational Health
  • Community and Home Care

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