Package: EmpiricalCalibration 3.1.4

Martijn Schuemie

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. For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.

Authors:Martijn Schuemie [aut, cre], Marc Suchard [aut]

EmpiricalCalibration_3.1.4.tar.gz
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EmpiricalCalibration_3.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
EmpiricalCalibration/json (API)
NEWS

# Install 'EmpiricalCalibration' in R:
install.packages('EmpiricalCalibration', repos = c('https://ohdsi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ohdsi/empiricalcalibration/issues

Pkgdown/docs site:https://ohdsi.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • caseControl - Odds ratios from a case-control design
  • cohortMethod - Relative risks from a new-user cohort design
  • grahamReplication - Relative risks from an adjusted new-user cohort design
  • sccs - Incidence rate ratios from Self-Controlled Case Series
  • southworthReplication - Relative risks from an unadjusted new-user cohort design

On CRAN:

Conda:

hadescpp

8.32 score 10 stars 5 packages 154 scripts 633 downloads 1 mentions 28 exports 19 dependencies

Last updated from:03d4747fd8. Checks:11 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
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linux-devel-x86_64WARNING185
source / vignettesOK236
linux-release-arm64WARNING183
linux-release-x86_64WARNING212
macos-release-arm64WARNING151
macos-release-x86_64WARNING332
macos-oldrel-arm64WARNING161
macos-oldrel-x86_64WARNING281
windows-develWARNING219
windows-releaseWARNING190
windows-oldrelWARNING205
wasm-releaseOK131

Exports:calibrateConfidenceIntervalcalibrateLlrcalibratePcompareEasecomputeCvBinomialcomputeCvPoissoncomputeCvPoissonRegressioncomputeExpectedAbsoluteSystematicErrorcomputeTraditionalCicomputeTraditionalPconvertNullToErrorModelevaluateCiCalibrationfitMcmcNullfitNullfitNullNonNormalLlfitSystematicErrorModelplotCalibrationplotCalibrationEffectplotCiCalibrationplotCiCalibrationEffectplotCiCoverageplotErrorModelplotExpectedType1ErrorplotForestplotMcmcTraceplotTrueAndObservedsimulateControlssimulateMaxSprtData

Dependencies:clicpp11farverggplot2gluegridExtragtableisobandlabelinglifecycleR6RColorBrewerRcpprlangS7scalesvctrsviridisLitewithr

Empirical calibration and MaxSPRT

Martijn J. Schuemie

Rendered fromEmpiricalMaxSprtCalibrationVignette.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2022-08-08
Started: 2021-09-24

Empirical calibration of confidence intervals

Martijn J. Schuemie, Marc A. Suchard

Rendered fromEmpiricalCiCalibrationVignette.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2022-01-28
Started: 2017-05-01

Empirical calibration of p-values

Martijn J. Schuemie, Marc A. Suchard

Rendered fromEmpiricalPCalibrationVignette.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2022-01-28
Started: 2017-05-01

Readme and manuals

Help Manual

Help pageTopics
Calibrate confidence intervalscalibrateConfidenceInterval
Calibrate the log likelihood ratiocalibrateLlr
Calibrate the p-valuecalibrateP calibrateP.mcmcNull calibrateP.null
Odds ratios from a case-control designcaseControl
Relative risks from a new-user cohort designcohortMethod
Compare EASE of correlated sets of estimatescompareEase
Compute critical values for Binomial datacomputeCvBinomial
Compute critical values for Poisson datacomputeCvPoisson
Compute critical values for Poisson regression datacomputeCvPoissonRegression
Compute the expected absolute systematic errorcomputeExpectedAbsoluteSystematicError
Compute the (traditional) confidence intervalcomputeTraditionalCi
Compute the (traditional) p-valuecomputeTraditionalP
Convert empirical null distribution to systematic error modelconvertNullToErrorModel
Evaluate confidence interval calibrationevaluateCiCalibration
Fit the null distribution using MCMCfitMcmcNull
Fit the null distributionfitNull
Fit the null distribution using non-normal log-likelihood approximationsfitNullNonNormalLl
Fit a systematic error modelfitSystematicErrorModel
Relative risks from an adjusted new-user cohort designgrahamReplication
Create a calibration plotplotCalibration
Plot the effect of the calibrationplotCalibrationEffect
Create a confidence interval calibration plotplotCiCalibration
Plot the effect of the CI calibrationplotCiCalibrationEffect
Create a confidence interval coverage plotplotCiCoverage
Plot the systematic error modelplotErrorModel
Plot the expected type 1 error as a function of standard errorplotExpectedType1Error
Create a forest plotplotForest
Plot the MCMC traceplotMcmcTrace
Plot true and observed valuesplotTrueAndObserved
Incidence rate ratios from Self-Controlled Case Seriessccs
Simulate (negative) controlssimulateControls
Simulate survival data for MaxSPRT computationsimulateMaxSprtData
Relative risks from an unadjusted new-user cohort designsouthworthReplication