plot_power

Plot power for each prior distribution according to selected simulation parameters

method

A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.

Isaac Gravestock

psborrow

Bayesian Dynamic Borrowing with Propensity Score

Craig Gower-Page

Matt Secrest

Yichen Lu

Aijing Lin

F. Hoffmann-La Roche AG 

plot_power function

<dl><dt>dt</dt>
<dd>a <code>data.frame</code> containing summary statistics for the posterior samples from each simulation generated with <code>get_summary</code>.</dd>
<dt>HR</dt>
<dd>pre-specified HR between treatment and control arm in the internal trial for which the power should be plotted. Must be within <code>unique(dt$HR)</code>.</dd>
<dt>driftHR</dt>
<dd>pre-specified HR between external control arm and internal control arm for which the power should be plotted. Must be within <code>unique(dt$driftHR)</code>.</dd>
<dt>pred</dt>
<dd>predictors to use when fitting exponential distribution in MCMC for which the power should be plotted. Must be within <code>unique(dt$pred)</code>.</dd></dl>

Arguments

Plot power — plot_power

<dl>

<dt>dt</dt>
<dd>a <code>data.frame</code> containing summary statistics for the posterior samples from each simulation generated with <code>get_summary</code>.</dd>


<dt>HR</dt>
<dd>pre-specified HR between treatment and control arm in the internal trial for which the power should be plotted. Must be within <code>unique(dt$HR)</code>.</dd>


<dt>driftHR</dt>
<dd>pre-specified HR between external control arm and internal control arm for which the power should be plotted. Must be within <code>unique(dt$driftHR)</code>.</dd>


<dt>pred</dt>
<dd>predictors to use when fitting exponential distribution in MCMC for which the power should be plotted. Must be within <code>unique(dt$pred)</code>.</dd>

</dl>

plot_power: Plot power

Description

Usage

Value

Arguments