PS-Integrated Methods for Incorporating Real-World Evidence in Clinical Studies
Distance between two distributions
Plot PS distributions
Plot PS distributions
Plot estimation results for power prior approach
Print borrow information
Print PS estimation results
Print PS estimation results
Print outcome analysis results
Print estimation results
Get number of subjects borrowed from each statum
Confidence/Credible Interval for PS-Integrated Estimation
PS-Integrated Composite Likelihood Estimation
Estimate propensity scores
Inference for the PS-Integrated Estimation
PS matching
Outcome Analysis for PS-Integrated Estimation
Get posterior samples based on PS-power prior approach (WATT)
Get posterior samples based on PS-power prior approach
PS-Integrated Kaplan-Meier Estimation
PS-Integrated Log-Rank Test For Comparing Time-to-event Outcomes
PS-Integrated Restricted Mean Survival Time (RMST) Test For Comparing ...
PS-Integrated Methods for Incorporating RWE in Clinical Studies
Composite Likelihood Estimation
Create strata
Kaplan-Meier Estimation
Log-rank Estimation
RMST Estimation
Call STAN models
Summarize PS estimation and matching results
Summarize PS estimation and stratification results
Summary outcome analysis results
Summarize overall 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>.