OutcomeContinuousNormal-class

.outcome_cont_normal

A class for defining a regression with a normal outcome
to be translated to Stan code.
Objects of class <code>OutcomeContinuousNormal</code> should not be created directly but by
the constructor <code>outcome_cont_normal()</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. 

OutcomeContinuousNormal-class function

<dl>
<dt><code>n_param</code></dt>
<dd>integer. Number of ancillary parameters for the model to estimate (0).</dd></dl>
<dt><code>param_priors</code></dt>
<dd>list. Named list of prior distributions on the ancillary parameters in the model.
Empty for <code>OutcomeContinuousNormal</code>.</dd>
<dt><code>continuous_var</code></dt>
<dd>character. Variable used for outcome in <code>OutcomeContinuousNormal</code> objects.</dd>
<dt><code>baseline_prior</code></dt>
<dd><code>Prior</code>. Object of class <code>Prior</code>
specifying prior distribution for the baseline outcome.</dd>
<dt><code>name_beta_trt.</code></dt>
<dd>Named vector for beta_trt.</dd>
<dt><code>alpha_type.</code></dt>
<dd>How to interpret alpha.</dd>
<dt><code>name_addnl_params.</code></dt>
<dd>Named vector for additional parameters.</dd>

Slots

OutcomeContinuousNormal class — OutcomeContinuousNormal-class

<dl>
<dt><code>n_param</code></dt>
<dd>integer. Number of ancillary parameters for the model to estimate (0).</dd>


<dt><code>param_priors</code></dt>
<dd>list. Named list of prior distributions on the ancillary parameters in the model.
Empty for <code>OutcomeContinuousNormal</code>.</dd>


<dt><code>continuous_var</code></dt>
<dd>character. Variable used for outcome in <code>OutcomeContinuousNormal</code> objects.</dd>


<dt><code>baseline_prior</code></dt>
<dd><code>Prior</code>. Object of class <code>Prior</code>
specifying prior distribution for the baseline outcome.</dd>


<dt><code>name_beta_trt.</code></dt>
<dd>Named vector for beta_trt.</dd>


<dt><code>alpha_type.</code></dt>
<dd>How to interpret alpha.</dd>


<dt><code>name_addnl_params.</code></dt>
<dd>Named vector for additional parameters.</dd>


</dl>

<code>OutcomeContinuousNormal</code> class

OutcomeContinuousNormal-class: `OutcomeContinuousNormal` class

Description

Arguments

Slots

See Also