surv2

A survival dataset related the long2 dataset, with different numeric and categorical covariate

datasets

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.

Atanu Bhattacharjee

jmBIG

Joint Longitudinal and Survival Model for Big Data

Bhrigu Kumar Rajbongshi

Gajendra K Vishwakarma

surv2 function

a tibble of 13 columns and 1000 observations,<dl>
<dt>id</dt>
<dd>id value for subjects</dd><dt>status</dt>
<dd>survival status</dd><dt>time</dt>
<dd>survival time</dd><dt>visit</dt>
<dd>visit time of longitudinal measurements</dd><dt>x1,x2,...,x7</dt>
<dd>different numeric and categorical variable</dd></dl>

Format

survival data — surv2

a tibble of 13 columns and 1000 observations,<dl>
<dt>id</dt>
<dd>id value for subjects</dd>

<dt>status</dt>
<dd>survival status</dd>

<dt>time</dt>
<dd>survival time</dd>

<dt>visit</dt>
<dd>visit time of longitudinal measurements</dd>

<dt>x1,x2,...,x7</dt>
<dd>different numeric and categorical variable</dd>



</dl>

surv2: survival data

Description

Usage

Arguments

Format