Joint Longitudinal and Survival Model for Big Data
Bootstrapped CI using FastJM
Joint model for BIG data using JMbayes2
Joint model for BIG data using FastJM
Joint model for BIG data using rstanarm
Joint model for BIG data using joineRML
Plot for cisurvfitJMCS object
Prediction using rstanarm
Prediction using rstanarm
Prediction using JMbayes2
Prediction using joineRML
print.jmbayesBig
print.jmcsBig
print.jmstanBig
print.joinRMLBig
Prediction using FastJM
Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences.