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

DOI10.1111/biom.12232zbMath1419.62313OpenAlexW1934193819WikidataQ46366148 ScholiaQ46366148MaRDI QIDQ3465731

Lucie Loubère, Jean-François Dartigues, Paul Blanche, Claudine Berr, Hélène Jacqmin-Gadda, Cécile Proust-Lima

Publication date: 22 January 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.12232

zbMATH Keywords

longitudinal datacompeting risksjoint modelprediction accuracydynamic prediction

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Related Items (5)

Review and Comparison of Computational Approaches for Joint Longitudinal and Time‐to‐Event Models ⋮ Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data ⋮ The Wally plot approach to assess the calibration of clinical prediction models ⋮ Missing link survival analysis with applications to available pandemic data ⋮ Comparison of joint modelling and landmarking approaches for dynamic prediction using bootstrap simulation

Uses Software

• timereg
• dynpred

Cites Work

• Unnamed Item
• Dynamic regression models for survival data.
• Joint Models for Longitudinal and Time-to-Event Data
• Choice of Prognostic Estimators in Joint Models by Estimating Differences of Expected Conditional Kullback-Leibler Risks
• Evaluating Prognostic Accuracy of Biomarkers under Competing Risk
• Combining Several Screening Tests: Optimality of the Risk Score
• Quantifying the predictive accuracy of time-to-event models in the presence of competing risks
• Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data
• Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time-Dependent Covariates
• Estimation methods for time‐dependent AUC models with survival data
• Landmark Prediction of Long-Term Survival Incorporating Short-Term Event Time Information
• Identification and efficacy of longitudinal markers for survival

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