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.
and Hodges, James S
2 Pith papers cite this work. Polarity classification is still indexing.
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stat.ME 2years
2026 2verdicts
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Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
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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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Restricted Multivariate Spatial Modeling
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.