Package: psrwe
Title: PS-Integrated Methods for Incorporating RWE in Clinical Studies
Version: 1.2
Author: Chenguang Wang [aut, cre]
        Trustees of Columbia University [cph] (tools/make_cpp.R, R/stanmodels.R)
Maintainer: Chenguang Wang <cwang68@jhmi.edu>
Description: 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>.
Depends: methods, R (>= 4.0), rstan (>= 2.19.3), Rcpp (>= 1.0.5)
License: GPL (>= 3)
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)
RcppModules: stan_fit4powerp_mod, stan_fit4powerps_mod,
        stan_fit4powerpsbinary_mod, stan_fit4prior_mod
Imports: parallel (>= 3.2), cowplot (>= 1.0.0), dplyr (>= 0.8.5),
        ggplot2 (>= 3.3.2), randomForest (>= 4.6-14)
Suggests: knitr, markdown
Encoding: UTF-8
LazyData: true
ByteCompile: true
SystemRequirements: GNU make
NeedsCompilation: yes
RoxygenNote: 7.1.0
VignetteBuilder: knitr
Packaged: 2020-09-01 12:44:22 UTC; cwang68
Repository: CRAN
Date/Publication: 2020-09-08 08:20:08 UTC
