.input_check

Checks inputs before Gibbs sampler is run

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

.input_check function

<dl><dt>Y</dt>
<dd>current time-to-event data</dd>
<dt>Y_0</dt>
<dd>historical time-to-event data</dd>
<dt>X</dt>
<dd>design Matrix</dd>
<dt>X_0</dt>
<dd>design Matrix for historical data</dd>
<dt>tuning_parameters</dt>
<dd>list of tuning parameters</dd>
<dt>initial_values</dt>
<dd>list of initial values (optional)</dd>
<dt>hyperparameters</dt>
<dd>list of hyperparameters</dd></dl>

Arguments

Input checker — .input_check

<dl>

<dt>Y</dt>
<dd>current time-to-event data</dd>


<dt>Y_0</dt>
<dd>historical time-to-event data</dd>


<dt>X</dt>
<dd>design Matrix</dd>


<dt>X_0</dt>
<dd>design Matrix for historical data</dd>


<dt>tuning_parameters</dt>
<dd>list of tuning parameters</dd>


<dt>initial_values</dt>
<dd>list of initial values (optional)</dd>


<dt>hyperparameters</dt>
<dd>list of hyperparameters</dd>

</dl>

.input_check: Input checker

Description

Usage

Value

Arguments