Semi-Parametric Joint Modeling of Longitudinal and Survival Data
A metric of prediction accuracy of joint model by comparing the predic...
Joint modeling of multivariate longitudinal and competing risks data
A metric of prediction accuracy of joint model by comparing the predic...
Fitted values for joint models
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Random effects estimates for joint models
Residuals for joint models
Joint modeling of multivariate longitudinal and competing risks data
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Anova Method for Fitted Joint Models
Prediction in Joint Models
Prediction in Joint Models
Variance-covariance matrix of the estimated parameters for joint model...
Estimated coefficients estimates for joint models
Joint modeling of longitudinal continuous data and competing risks
Anova Method for Fitted Joint Models
Time-dependent AUC/Cindex for joint models
Fitted values for joint models
A joint model for large-scale, competing risks time-to-event data with singular or multiple longitudinal biomarkers, implemented with the efficient algorithms developed by Li and colleagues (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal biomarkers are modelled using a linear mixed effects model. The association between the longitudinal submodel and the survival submodel is captured through shared random effects. It allows researchers to analyze large-scale data to model biomarker trajectories, estimate their effects on event outcomes, and dynamically predict future events from patients’ past histories. A function for simulating survival and longitudinal data for multiple biomarkers is also included alongside built-in datasets.