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) , Wang et al. (2020) and Chen et al. (2020) .


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1.3 by Chenguang Wang, 8 months ago

Browse source code at

Authors: Chenguang Wang [aut, cre] Trustees of Columbia University [cph] (tools/make_cpp.R , R/stanmodels.R)

Documentation:   PDF Manual  

GPL (>= 3) license

Imports parallel, cowplot, dplyr, ggplot2, randomForest

Depends on methods, rstan, Rcpp

Suggests knitr, markdown

Linking to BH, rstan, Rcpp, RcppEigen, StanHeaders

System requirements: GNU make

See at CRAN