psrwe: PS-Integrated Methods for Incorporating RWE in Clinical Studies

High-quality real-world data can be transformed into scientific real-world evidence (RWE) for regulatory and healthcare decision-making using proven analytical methods and techniques. For example, propensity score (PS) methodology can be applied to pre-select a subset of real-world data containing patients that are similar to those in the current clinical study in terms of covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. Then, methods such as 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 RWE analysis methods proposed in 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>.

Version: 1.2
Depends: methods, R (≥ 4.0), rstan (≥ 2.19.3), Rcpp (≥ 1.0.5)
Imports: parallel (≥ 3.2), cowplot (≥ 1.0.0), dplyr (≥ 0.8.5), ggplot2 (≥ 3.3.2), randomForest (≥ 4.6-14)
LinkingTo: BH (≥ 1.72.0-3), rstan (≥ 2.19.3), Rcpp (≥ 1.0.5), RcppEigen (≥ 0.3.3.7.0), StanHeaders (≥ 2.21.0-5)
Suggests: knitr, markdown
Published: 2020-09-08
Author: Chenguang Wang [aut, cre] Trustees of Columbia University [cph] (tools/make_cpp.R, R/stanmodels.R)
Maintainer: Chenguang Wang <cwang68 at jhmi.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
SystemRequirements: GNU make
CRAN checks: psrwe results

Downloads:

Reference manual: psrwe.pdf
Vignettes: psrwe: Propensity Score-Integrated Methods for Incorporating Real-World Evidence in Clinical Studies
Package source: psrwe_1.2.tar.gz
Windows binaries: r-devel: psrwe_1.2.zip, r-release: psrwe_1.2.zip, r-oldrel: not available
macOS binaries: r-release: psrwe_1.2.tgz, r-oldrel: not available

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