jmBIG: Joint Longitudinal and Survival Model for Big Data
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.
||R (≥ 2.10)
||JMbayes2, joineRML, rstanarm, FastJM, dplyr, nlme, survival
||Atanu Bhattacharjee [aut, cre, ctb],
Bhrigu Kumar Rajbongshi [aut, ctb],
Gajendra K Vishwakarma [aut, ctb]
||Atanu Bhattacharjee <atanustat at gmail.com>
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