Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.
Version: 3.1.4 Depends: R (≥ 3.5.0) Imports: ggplot2 (≥ 2.0.0), gridExtra, methods, rlang, Rcpp LinkingTo: Rcpp Suggests: knitr, markdown, rmarkdown, testthat, Cyclops, survival, Sequential Published: 2025-02-14 DOI: 10.32614/CRAN.package.EmpiricalCalibration Author: Martijn Schuemie [aut, cre], Marc Suchard [aut] Maintainer: Martijn Schuemie <schuemie at ohdsi.org> BugReports: https://github.com/OHDSI/EmpiricalCalibration/issues License: Apache License 2.0 URL: https://ohdsi.github.io/EmpiricalCalibration/, https://github.com/OHDSI/EmpiricalCalibration NeedsCompilation: yes Citation: EmpiricalCalibration citation info Materials: README NEWS CRAN checks: EmpiricalCalibration results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=EmpiricalCalibration to link to this page.
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