EmpiricalCalibration: Routines for Performing Empirical Calibration of Observational Study Estimates

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.

Version: 2.0.2
Imports: ggplot2 (≥ 2.0.0), gridExtra, methods
Suggests: knitr, rmarkdown
Published: 2020-04-07
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: no
Citation: EmpiricalCalibration citation info
Materials: README NEWS
CRAN checks: EmpiricalCalibration results


Reference manual: EmpiricalCalibration.pdf
Vignettes: Empirical calibration of confidence intervals
Empirical calibration of p-values
Package source: EmpiricalCalibration_2.0.2.tar.gz
Windows binaries: r-devel: EmpiricalCalibration_2.0.2.zip, r-release: EmpiricalCalibration_2.0.2.zip, r-oldrel: EmpiricalCalibration_2.0.2.zip
macOS binaries: r-release: EmpiricalCalibration_2.0.2.tgz, r-oldrel: EmpiricalCalibration_2.0.2.tgz
Old sources: EmpiricalCalibration archive


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