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Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks

Biometrics, 2015-03, Vol.71 (1), p.102-113 [Peer Reviewed Journal]

Copyright © 2015 International Biometric Society ;2014, The International Biometric Society ;2014, The International Biometric Society. ;ISSN: 0006-341X ;EISSN: 1541-0420 ;DOI: 10.1111/biom.12232 ;PMID: 25311240 ;CODEN: BIOMA5

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Title:
Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks
Author: Blanche, Paul ; Proust‐Lima, Cécile ; Loubère, Lucie ; Berr, Claudine ; Dartigues, Jean‐François ; Jacqmin‐Gadda, Hélène
Subjects: Accuracy ; BIOMETRIC METHODOLOGY ; biometry ; Cohort Studies ; Competing risks ; Computer Simulation ; confidence interval ; Confidence intervals ; death ; Dementia ; Dementia - diagnosis ; Dementia - mortality ; Dynamic prediction ; elderly ; Humans ; Joint model ; Longitudinal data ; Models, Statistical ; prediction ; Prediction accuracy ; Prevalence ; Psychological tests ; Psychometrics - methods ; Reproducibility of Results ; risk ; Risk Assessment - methods ; risk groups ; Sensitivity and Specificity ; statistical analysis ; Statistical methods ; Survival Analysis
Is Part Of: Biometrics, 2015-03, Vol.71 (1), p.102-113
Description: 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.
Publisher: United States: International Biometric Society, etc.
Language: English;French
Identifier: ISSN: 0006-341X
EISSN: 1541-0420
DOI: 10.1111/biom.12232
PMID: 25311240
CODEN: BIOMA5
Source: MEDLINE
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Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks

Copyright © 2015 International Biometric Society ;2014, The International Biometric Society ;2014, The International Biometric Society. ;ISSN: 0006-341X ;EISSN: 1541-0420 ;DOI: 10.1111/biom.12232 ;PMID: 25311240 ;CODEN: BIOMA5

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