.beta_MH_RW

Update beta via a Metropolis-Hastings Random Walk move

Allows Bayesian borrowing from a historical dataset for time-to-
event data. A flexible baseline hazard function is achieved via a piecewise
exponential likelihood with time varying split points and smoothing prior on the
historic baseline hazards. The method is described in Scott and Lewin (2024)
<doi:10.48550/arXiv.2401.06082>, and the software paper is in Axillus et al.
(2024) <doi:10.48550/arXiv.2408.04327>.

Darren Scott

BayesFBHborrow

Bayesian Dynamic Borrowing with Flexible Baseline Hazard
Function

Sophia Axillus

.beta_MH_RW function

<dl><dt>df</dt>
<dd>data.frame from dataframe_fun()</dd>
<dt>beta</dt>
<dd>beta values</dd>
<dt>bp</dt>
<dd>number of covariates</dd>
<dt>cprop_beta</dt>
<dd>hyperparameter for beta proposal standard deviation</dd>
<dt>beta_count</dt>
<dd>number of moves done for beta</dd></dl>

Arguments

Beta Metropolis-Hastings random walk move — .beta_MH_RW

<dl>

<dt>df</dt>
<dd>data.frame from dataframe_fun()</dd>


<dt>beta</dt>
<dd>beta values</dd>


<dt>bp</dt>
<dd>number of covariates</dd>


<dt>cprop_beta</dt>
<dd>hyperparameter for beta proposal standard deviation</dd>


<dt>beta_count</dt>
<dd>number of moves done for beta</dd>

</dl>

.beta_MH_RW: Beta Metropolis-Hastings random walk move

Description

Usage

Value

Arguments