logistic_bin_outcome

Please use <code>outcome_bin_logistic()</code> instead.

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. 

logistic_bin_outcome function

<dl><dt>...</dt>
<dd>Deprecated arguments to <code>logistic_bin_outcome</code>.</dd></dl>

Arguments

Legacy function for binary logistic regression — logistic_bin_outcome

<dl>

<dt>...</dt>
<dd>Deprecated arguments to <code>logistic_bin_outcome</code>.</dd>

</dl>

logistic_bin_outcome: Legacy function for binary logistic regression

Description

Usage

Value

Arguments