Optimizing dynamic predictions from joint models using super learning

DOI10.1002/sim.10010zbMATH Open1545.62519MaRDI QIDQ6618415

Jeremy M. G. Taylor, Dimitris Rizopoulos

Publication date: 14 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

survival analysiscross-entropyBrier scoretime-varying covariatesprecision medicineprognostic models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Unnamed Item
• Generating random correlation matrices based on vines and extended onion method
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Stacked regressions
• 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
• Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data
• The cross-validated adaptive epsilon-net estimator
• Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks
• Choosing a Strictly Proper Scoring Rule
• Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models
• Super Learner
• Identification and efficacy of longitudinal markers for survival

This page was built for publication: Optimizing dynamic predictions from joint models using super learning