rwe_stan

Call STAN models. Called by <code>psrwe_powerp</code>.

High-quality real-world data can be transformed into scientific
real-world evidence for regulatory and healthcare decision-making
using proven analytical methods and techniques. For example, propensity
score (PS) methodology can be applied to select a subset of real-world
data containing patients that are similar to those in the current clinical
study in terms of baseline covariates, and to stratify the selected
patients together with those in the current study into more homogeneous
strata. Then, statistical methods such as the power prior approach or
composite likelihood approach can be applied in each stratum to draw
inference for the parameters of interest. This package provides
functions that implement the PS-integrated real-world
evidence analysis methods such as
Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>,
Wang et al. (2020) <doi:10.1080/10543406.2019.1684309>, and
Chen et al. (2020) <doi:10.1080/10543406.2020.1730877>.

Wei-Chen Chen

psrwe

PS-Integrated Methods for Incorporating Real-World Evidence in
Clinical Studies

Chenguang Wang

Trustees of Columbia University 

rwe_stan function

<dl><dt>lst_data</dt>
<dd>List of study data to be passed to STAN</dd>
<dt>stan_mdl</dt>
<dd>STAN model including<dl>
 <dt>powerps</dt>
<dd>PS-power prior model for continuous outcomes</dd></dl></dd> <dt>powerpsbinary</dt>
<dd>PS-power prior model for binary outcomes</dd> <dt>powerp</dt>
<dd>Power prior model</dd>
</dl>
<dt>chains</dt>
<dd>STAN parameter. Number of Markov chains</dd>
<dt>iter</dt>
<dd>STAN parameter. Number of iterations</dd>
<dt>warmup</dt>
<dd>STAN parameter. Number of burn-in.</dd>
<dt>control</dt>
<dd>STAN parameter. See <code>rstan::stan</code> for details.</dd>
<dt>...</dt>
<dd>other options to call STAN sampling such as <code>thin</code>,
<code>algorithm</code>. See <code>rstan::sampling</code> for details.#'</dd>

Arguments

Call STAN models — rwe_stan

<dl>

<dt>lst_data</dt>
<dd>List of study data to be passed to STAN</dd>


<dt>stan_mdl</dt>
<dd>STAN model including<dl>
 <dt>powerps</dt>
<dd>PS-power prior model for continuous outcomes</dd>

 <dt>powerpsbinary</dt>
<dd>PS-power prior model for binary outcomes</dd>

 <dt>powerp</dt>
<dd>Power prior model</dd>


</dl></dd>


<dt>chains</dt>
<dd>STAN parameter. Number of Markov chains</dd>


<dt>iter</dt>
<dd>STAN parameter. Number of iterations</dd>


<dt>warmup</dt>
<dd>STAN parameter. Number of burn-in.</dd>


<dt>control</dt>
<dd>STAN parameter. See <code>rstan::stan</code> for details.</dd>


<dt>...</dt>
<dd>other options to call STAN sampling such as <code>thin</code>,
<code>algorithm</code>. See <code>rstan::sampling</code> for details.#'</dd>

</dl>

rwe_stan: Call STAN models

Description

Usage

Value

Arguments