.dataframe_fun

Construct a split data.frame for updated split points

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

.dataframe_fun function

<dl><dt>Y</dt>
<dd>time-to-event</dd>
<dt>I</dt>
<dd>censor indicator</dd>
<dt>X</dt>
<dd>design Matrix</dd>
<dt>s</dt>
<dd>split point locations, including start and end (length J + 2)</dd>
<dt>lambda</dt>
<dd>baseline Hazards (length J+1)</dd>
<dt>bp</dt>
<dd>number of covariates</dd>
<dt>J</dt>
<dd>number of split points</dd></dl>

Arguments

Create data.frame for piecewise exponential models — .dataframe_fun

<dl>

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


<dt>I</dt>
<dd>censor indicator</dd>


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


<dt>s</dt>
<dd>split point locations, including start and end (length J + 2)</dd>


<dt>lambda</dt>
<dd>baseline Hazards (length J+1)</dd>


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


<dt>J</dt>
<dd>number of split points</dd>

</dl>

.dataframe_fun: Create data.frame for piecewise exponential models

Description

Usage

Value

Arguments