c(person("Dave", "Armstrong", role=c("aut", "cre"), email="dave@quantoid.net"),
person("Robert", "Andersen", role="aut"),
person("Justin", "Esarey", role="cph"),
person("John", "Fox", role="cph"),
person("Michael", "Friendly", role="cph"),
person("Adrian", "Bowman", role="cph"),
person("Adelchi", "Azzalini", role="cph"))
Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, 978-1446269800).
stats, MASS, metap, car, nortest, lawstat, normwhn.test,
DAMisc, ggplot2, boot, grid, cowplot, fANCOVA, dplyr, tidyr,
utils, magrittr, tibble, sm, multcomp, rlang, ggrepel, mgcv
Dave Armstrong [aut, cre],
Robert Andersen [aut],
Justin Esarey [cph],
John Fox [cph],
Michael Friendly [cph],
Adrian Bowman [cph],
Adelchi Azzalini [cph]

stats, MASS, metap, car, nortest, lawstat, DAMisc, ggplot2,
boot, grid, cowplot, fANCOVA, dplyr, tidyr, utils, magrittr,
tibble, sm, multcomp, rlang, ggrepel, mgcv

PS-Integrated Methods for Incorporating RWE in Clinical Studies
Chenguang Wang [aut, cre]
Trustees of Columbia University [cph] (tools/make_cpp.R, R/stanmodels.R)
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>.