SurvData

This dataset contains survival outcome.

data

The shared random effects joint model is one of the most widely used approaches to study the associations between longitudinal biomarkers and a survival outcome and make dynamic risk predictions using the longitudinally measured biomarkers.
One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large.
This package can be used to fit complex multivariate joint models using our newly developed algorithm Jieqi Tu and Jiehuan Sun (2023) <doi:10.1002/sim.9619>, which is based on Gaussian variational approximate inference and is computationally efficient.

Jiehuan Sun

VBJM

Variational Inference for Joint Model

SurvData function

A data frame with 100 rows and 4 variables

Format

Jiehuan Sun <a href="/link/jiehuan.sun%40gmail.com?package=VBJM&version=0.1.0" data-mini-rdoc="VBJM::jiehuan.sun@gmail.com">jiehuan.sun@gmail.com</a>

Author

Simulated Survival Data — SurvData

Jiehuan Sun <a href='mailto:jiehuan.sun@gmail.com'>jiehuan.sun@gmail.com</a>

SurvData: Simulated Survival Data

Description

Usage

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Author

Details