add_covariates

Specify column names for adjustment variables in model matrix and prior
distributions for the model parameters for these covariates (i.e., betas)

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. 

add_covariates function

<dl><dt>covariates</dt>
<dd>character. Names of columns in the data matrix containing
covariates to be adjusted for in the outcome model. Note: the
external and treatment flags should not go here.</dd>
<dt>priors</dt>
<dd>Either a single object of class <code>Prior</code> specifying the prior
distribution to apply to all covariates or a named list of
distributions of class <code>Prior</code>, one for each covariate</dd></dl>

Arguments

Add Covariates for Model Adjustment — add_covariates

<dl>

<dt>covariates</dt>
<dd>character. Names of columns in the data matrix containing
covariates to be adjusted for in the outcome model. Note: the
external and treatment flags should not go here.</dd>


<dt>priors</dt>
<dd>Either a single object of class <code>Prior</code> specifying the prior
distribution to apply to all covariates or a named list of
distributions of class <code>Prior</code>, one for each covariate</dd>

</dl>

add_covariates: Add Covariates for Model Adjustment

Description

Usage

Value

Arguments

Examples