A landmarking approach using latent class mixed models for dynamic prediction of time-to-event data that accounts for latent heterogeneity in longitudinal biomarker trajectories.
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WRaPs extends optimally weighted random effect estimators to joint models, providing closed-form solutions for basic cases and MCMC computation for complex ones to predict extreme random effects while accounting for survival data.
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Landmarking with Latent Class Mixed Models for Dynamic Prediction of Time-to-event Data with Heterogeneous Biomarker Trajectories
A landmarking approach using latent class mixed models for dynamic prediction of time-to-event data that accounts for latent heterogeneity in longitudinal biomarker trajectories.
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Improved prediction of extreme random effects in joint models: WRaPs
WRaPs extends optimally weighted random effect estimators to joint models, providing closed-form solutions for basic cases and MCMC computation for complex ones to predict extreme random effects while accounting for survival data.