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  "Title": "PS-Integrated Methods for Incorporating Real-World Evidence in\nClinical Studies",
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  "Description": "High-quality real-world data can be transformed into\nscientific real-world evidence for regulatory and healthcare\ndecision-making using proven analytical methods and techniques.\nFor example, propensity score (PS) methodology can be applied\nto select a subset of real-world data containing patients that\nare similar to those in the current clinical study in terms of\nbaseline covariates, and to stratify the selected patients\ntogether with those in the current study into more homogeneous\nstrata. Then, statistical methods such as the power prior\napproach or composite likelihood approach can be applied in\neach stratum to draw inference for the parameters of interest.\nThis package provides functions that implement the\nPS-integrated real-world evidence analysis methods such as Wang\net al. (2019) <doi:10.1080/10543406.2019.1657133>, Wang et al.\n(2020) <doi:10.1080/10543406.2019.1684309>, and Chen et al.\n(2020) <doi:10.1080/10543406.2020.1730877>.",
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