High dimensional data on three consecutive measurements for and treatment arm information column.

datasets

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC).
Currently support mixed effect regression with or without missing observations by considering
covariance structures. It provides estimates by missing at random and missing not at random assumptions.
In this R package, we present Bayesian approaches that statisticians and clinical
researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by
Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.

Atanu Bhattacharjee

longit

High Dimensional Longitudinal Data Analysis Using MCMC

Akash Pawar

Bhrigu Kumar Rajbongshi

gh function

A <code>tibble</code> with 4 columns which are :<dl class="dl-horizontal">
<dt>y1</dt><dd>Observation on first timepoint</dd>
<dt>y2</dt><dd>Observation on second timepoint</dd>
<dt>y3</dt><dd>Observation on first timepoint</dd>
<dt>Treatment</dt><dd>Treatment arm of the patient</dd></dl>

Format

gh — gh

A <code>tibble</code> with 4 columns which are :<dl class='dl-horizontal'>
<dt>y1</dt><dd>Observation on first timepoint</dd>
<dt>y2</dt><dd>Observation on second timepoint</dd>
<dt>y3</dt><dd>Observation on first timepoint</dd>
<dt>Treatment</dt><dd>Treatment arm of the patient</dd></dl>

gh: gh

Description

Usage

Arguments

Format