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.

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. - In: EPIDEMIOLOGY BIOSTATISTICS AND PUBLIC HEALTH. - ISSN 2282-0930. - ELETTRONICO. - 11:4(2014). [10.2427/10003]

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

FARCOMENI, Alessio;Pasqualetti Patrizio;
2014

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.
2014
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.
01 Pubblicazione su rivista::01a Articolo in rivista
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. - In: EPIDEMIOLOGY BIOSTATISTICS AND PUBLIC HEALTH. - ISSN 2282-0930. - ELETTRONICO. - 11:4(2014). [10.2427/10003]
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/593452
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact