.lambda_conj_prop

Propose lambda from a gamma conditional conjugate posterior proposal

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

.lambda_conj_prop function

<dl><dt>df</dt>
<dd>data.frame from dataframe_fun()</dd>
<dt>beta</dt>
<dd>parameter value for beta</dd>
<dt>j</dt>
<dd>current split point</dd>
<dt>bp</dt>
<dd>number of covariates</dd>
<dt>alam</dt>
<dd>lambda hyperparameter, default set to 0.01</dd>
<dt>blam</dt>
<dd>lambda hyperparameter, default set to 0.01</dd></dl>

Arguments

Propose lambda from a gamma conditional conjugate posterior proposal — .lambda_conj_prop

<dl>

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


<dt>beta</dt>
<dd>parameter value for beta</dd>


<dt>j</dt>
<dd>current split point</dd>


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


<dt>alam</dt>
<dd>lambda hyperparameter, default set to 0.01</dd>


<dt>blam</dt>
<dd>lambda hyperparameter, default set to 0.01</dd>

</dl>

.lambda_conj_prop: Propose lambda from a gamma conditional conjugate posterior proposal

Description

Usage

Value

Arguments