psrwe_compl

Estimate the mean of the outcome based on PS-integrated composite likelihood
approach. Variance is estimated by the Jackknife method. Applies to the case
when there is only one external data source.

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 

psrwe_compl function

<dl><dt>dta_psbor</dt>
<dd>A class <code>PSRWE_BOR</code> object generated by
<code>psrwe_borrow</code>.</dd>
<dt>v_outcome</dt>
<dd>Column name corresponding to the outcome.</dd>
<dt>outcome_type</dt>
<dd>Type of outcomes: <code>continuous</code> or <code>binary</code>.</dd>
<dt>stderr_method</dt>
<dd>Method for computing StdErr, see Details</dd>
<dt>n_bootstrap</dt>
<dd>Number of bootstrap samples (for bootstrap stderr)</dd>
<dt>...</dt>
<dd>Parameters for <code>rwe_cl</code></dd></dl>

Arguments

PS-Integrated Composite Likelihood Estimation — psrwe_compl

<dl>

<dt>dta_psbor</dt>
<dd>A class <code>PSRWE_BOR</code> object generated by
<code>psrwe_borrow</code>.</dd>


<dt>v_outcome</dt>
<dd>Column name corresponding to the outcome.</dd>


<dt>outcome_type</dt>
<dd>Type of outcomes: <code>continuous</code> or <code>binary</code>.</dd>


<dt>stderr_method</dt>
<dd>Method for computing StdErr, see Details</dd>


<dt>n_bootstrap</dt>
<dd>Number of bootstrap samples (for bootstrap stderr)</dd>


<dt>...</dt>
<dd>Parameters for <code>rwe_cl</code></dd>

</dl>

psrwe_compl: PS-Integrated Composite Likelihood Estimation

Description

Usage

Value

Arguments

Details

Examples