.set_tuning_parameters

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

.set_tuning_parameters function

<dl><dt>tuning_parameters</dt>
<dd>list of tuning_parameters, could contain any combination
of the listed tuning parameters</dd>
<dt>borrow</dt>
<dd>choice of borrow, could be TRUE or FALSE</dd>
<dt>X</dt>
<dd>design matrix for concurrent trial</dd>
<dt>X_0</dt>
<dd>design matrix for historical trial</dd></dl>

Arguments

Set tuning parameters — .set_tuning_parameters

<dl>

<dt>tuning_parameters</dt>
<dd>list of tuning_parameters, could contain any combination
of the listed tuning parameters</dd>


<dt>borrow</dt>
<dd>choice of borrow, could be TRUE or FALSE</dd>


<dt>X</dt>
<dd>design matrix for concurrent trial</dd>


<dt>X_0</dt>
<dd>design matrix for historical trial</dd>

</dl>

.set_tuning_parameters: Set tuning parameters

Description

Usage

Value

Arguments