title: Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks.
abstract: Thanks to the growing interest in personalized medicine, joint modeling of longitudinal marker and time-to-event data has recently started to be used to derive dynamic individual risk predictions. Individual predictions are called dynamic because they are updated when information on the subject's health profile grows with time. We focus in this work on statistical methods for quantifying and comparing dynamic predictive accuracy of this kind of prognostic models, accounting for right censoring and possibly competing events. Dynamic area under the ROC curve (AUC) and Brier Score (BS) are used to quantify predictive accuracy. Nonparametric inverse probability of censoring weighting is used to estimate dynamic curves of AUC and BS as functions of the time at which predictions are made. Asymptotic results are established and both pointwise confidence intervals and simultaneous confidence bands are derived. Tests are also proposed to compare the dynamic prediction accuracy curves of two prognostic models. The finite sample behavior of the inference procedures is assessed via simulations. We apply the proposed methodology to compare various prediction models using repeated measures of two psychometric tests to predict dementia in the elderly, accounting for the competing risk of death. Models are estimated on the French Paquid cohort and predictive accuracies are evaluated and compared on the French Three-City cohort.
authors.affiliation: Université Bordeaux Segalen, ISPED, Inserm Research Center U897, F33076 Bordeaux, France.|INSERM, ISPED, Centre INSERM U897-Epidémiologie-Biostatistique, F-33000 Bordeaux, France.|Department of Biostatistics, University of Copenhagen, DK-1014 Copenhagen K, Denmark.
authors.forename: Paul
authors.initials: P
authors.lastname: Blanche
authors.affiliation: Université Bordeaux Segalen, ISPED, Inserm Research Center U897, F33076 Bordeaux, France.|INSERM, ISPED, Centre INSERM U897-Epidémiologie-Biostatistique, F-33000 Bordeaux, France.
authors.forename: Cécile
authors.initials: C
authors.lastname: Proust-Lima
authors.affiliation: Université Bordeaux Segalen, ISPED, Inserm Research Center U897, F33076 Bordeaux, France.|INSERM, ISPED, Centre INSERM U897-Epidémiologie-Biostatistique, F-33000 Bordeaux, France.
authors.forename: Lucie
authors.initials: L
authors.lastname: Loubère
authors.affiliation: INSERM, Centre INSERM U1061, Université Montpellier 1, Montpellier, France.
authors.forename: Claudine
authors.initials: C
authors.lastname: Berr
authors.affiliation: Université Bordeaux Segalen, ISPED, Inserm Research Center U897, F33076 Bordeaux, France.|INSERM, ISPED, Centre INSERM U897-Epidémiologie-Biostatistique, F-33000 Bordeaux, France.
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Dartigues
authors.affiliation: Université Bordeaux Segalen, ISPED, Inserm Research Center U897, F33076 Bordeaux, France.|INSERM, ISPED, Centre INSERM U897-Epidémiologie-Biostatistique, F-33000 Bordeaux, France.
authors.forename: Hélène
authors.initials: H
authors.lastname: Jacqmin-Gadda
mesh_terms: D015331:Cohort Studies; D003198:Computer Simulation; D003704:Dementia / Q000175:diagnosis* / Q000401:mortality*; D006801:Humans; D015233:Models, Statistical; D015995:Prevalence; D011594:Psychometrics / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity; D016019:Survival Analysis
keywords: Competing risks; Dynamic prediction; Joint model; Longitudinal data; Prediction accuracy