Joint Model of Heterogeneous Repeated Measures and Survival Data
Time-dependent AUC for joint models
Joint Modeling for Continuous outcomes
A metric of prediction accuracy of joint model by comparing the predic...
A metric of prediction accuracy of joint model by comparing the predic...
Plot conditional probabilities for new subjects
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Prediction in Joint Models
Variance-covariance matrix of the estimated parameters for joint model...
Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data in the presence of heterogeneous within-subject variability, proposed by Li and colleagues (2023) <arXiv:2301.06584>. The proposed method models the within-subject variability of the biomarker and associates it with the risk of the competing risks event. The time-to-event data is modeled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modeled using a mixed-effects location and scale model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.