print.PSRWE_RST

Print summary information of outcome mean estimation results

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 

print.PSRWE_RST function

<dl><dt>x</dt>
<dd>A list of class <code>PSRWE_RST</code> that is generated using the
<code>psrwe_powerp</code>, <code>psrwe_compl</code>, or
<code>psrwe_survkm</code> function.</dd>
<dt>...</dt>
<dd>Additional parameters</dd></dl>

Arguments

Print estimation results — print.PSRWE_RST

<dl>

<dt>x</dt>
<dd>A list of class <code>PSRWE_RST</code> that is generated using the
<code>psrwe_powerp</code>, <code>psrwe_compl</code>, or
<code>psrwe_survkm</code> function.</dd>


<dt>...</dt>
<dd>Additional parameters</dd>

</dl>

print.PSRWE_RST: Print estimation results

Description

Usage

Value

Arguments

See Also