Treatment-class

.treatment_class

A class for defining treatment details. Objects of class
<code>Treatment</code> should not be created directly but by the constructor
<code>treatment_details()</code>.

Bayesian dynamic borrowing is an approach to incorporating external
data to supplement a randomized, controlled trial analysis in which
external data are incorporated in a dynamic way (e.g., based on similarity
of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview.
This package implements the hierarchical commensurate prior approach to dynamic borrowing
as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>.
There are three main functionalities. First, 'psborrow2' provides a user-friendly
interface for applying dynamic borrowing on the study results handles the Markov Chain
Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a
simulation framework to compare different borrowing parameters (e.g. full borrowing, no
borrowing, dynamic borrowing) and other trial and borrowing characteristics
(e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides
a set of functions to generate data for simulation studies, and also allows
the user to specify their own data generation process. This package is designed to
use the sampling functions from 'cmdstanr' which can be installed from
<https://stan-dev.r-universe.dev>.

Matt Secrest

psborrow2

Bayesian Dynamic Borrowing Analysis and Simulation

Isaac Gravestock

Craig Gower-Page

Manoj Khanal

Mingyang Shan

Kexin Jin

Zhi Yang

Genentech, Inc. 

Treatment-class function

<dl>
<dt><code>trt_flag_col</code></dt>
<dd>character. Character specifying the name of the column
in the model matrix that corresponds to the treatment flag
(<code>1</code>/<code>0</code> or <code>TRUE</code>/<code>FALSE</code>). This identifies patients as belonging
to the experimental treatment arm.</dd></dl>
<dt><code>trt_prior</code></dt>
<dd><code>Prior</code>. Object of class <code>Prior</code> specifying the
prior distribution of the log effect estimate (log hazard ratio for
time to event endpoints and log odds ratio for binary endpoints).</dd>

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Treatment Class — Treatment-class

<dl>
<dt><code>trt_flag_col</code></dt>
<dd>character. Character specifying the name of the column
in the model matrix that corresponds to the treatment flag
(<code>1</code>/<code>0</code> or <code>TRUE</code>/<code>FALSE</code>). This identifies patients as belonging
to the experimental treatment arm.</dd>


<dt><code>trt_prior</code></dt>
<dd><code>Prior</code>. Object of class <code>Prior</code> specifying the
prior distribution of the log effect estimate (log hazard ratio for
time to event endpoints and log odds ratio for binary endpoints).</dd>


</dl>

<code>Treatment</code> Class

Treatment-class: `Treatment` Class

Description

Arguments

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