add_mcmc

Generating posterior samples from MCMC

internal

method

A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.

Isaac Gravestock

psborrow

Bayesian Dynamic Borrowing with Propensity Score

Craig Gower-Page

Matt Secrest

Yichen Lu

Aijing Lin

F. Hoffmann-La Roche AG 

add_mcmc function

<dl><dt>dt</dt>
<dd>a list of <code>matrix</code> containing simulated time-to-events information</dd>
<dt>priorObj</dt>
<dd>an object of class <code>.priorClass</code> generated in <code>set_prior</code></dd>
<dt>n.chains</dt>
<dd>number of parallel chains for the model</dd>
<dt>n.adapt</dt>
<dd>number of iterations for adaptation</dd>
<dt>n.burn</dt>
<dd>number of iterations discarded as burn-in</dd>
<dt>n.iter</dt>
<dd>number of iterations to monitor</dd>
<dt>seed</dt>
<dd>the seed of random number generator. Default is the first element of .Random.seed</dd></dl>

Arguments

Generating posterior samples from MCMC — add_mcmc

<dl>

<dt>dt</dt>
<dd>a list of <code>matrix</code> containing simulated time-to-events information</dd>


<dt>priorObj</dt>
<dd>an object of class <code>.priorClass</code> generated in <code>set_prior</code></dd>


<dt>n.chains</dt>
<dd>number of parallel chains for the model</dd>


<dt>n.adapt</dt>
<dd>number of iterations for adaptation</dd>


<dt>n.burn</dt>
<dd>number of iterations discarded as burn-in</dd>


<dt>n.iter</dt>
<dd>number of iterations to monitor</dd>


<dt>seed</dt>
<dd>the seed of random number generator. Default is the first element of .Random.seed</dd>

</dl>

add_mcmc: Generating posterior samples from MCMC

Description

Usage

Value

Arguments