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.


EmpiricalCalibration

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Introduction

This R package contains 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, as described in the paper Interpreting observational studies: why empirical calibration is needed to correct p-values..

Features

  • Estimate the empirical null distribution given the effect estimates of a set of negative controls
  • Estimate the calibrated p-value of a given hypothesis given the estimated empirical null distribution
  • Produce various plots for evaluating the empirical calibration
  • Contains the data sets from the paper for illustration

Screenshots and examples

Calibration effect plot
data(sccs) #Load one of the included data sets
negatives <- sccs[sccs$groundTruth == 0,] #Select the negative controls
null <- fitNull(negatives$logRr,negatives$seLogRr) #Fit the null distribution
positive <- sccs[sccs$groundTruth == 1,]  #Select the positive control
plotCalibrationEffect(negatives$logRr,negatives$seLogRr,positive$logRr,positive$seLogRr,null)
 
#Compute the calibrated p-value:
calibrateP(positive$logRr,positive$seLogRr, null) #Compute calibrated p-value
[1] 0.8390598

Technology

This is a pure R package.

System requirements

Requires R (version 3.1.0 or newer).

Getting Started

In R, use the following commands to install the latest stable version from CRAN:

install.packages("EmpiricalCalibration")

To install the latest development version directly from GitHub, use:

install.packages("devtools")
library(devtools)
install_github("ohdsi/EmpiricalCalibration")

Getting Involved

License

EmpiricalCalibration is licensed under Apache License 2.0

Development

This package has been developed in RStudio.

Development status

Build Status

This package is ready for use.

Acknowledgements

Martijn Schuemie is the author of this package.

News

EmpiricalCalibration v1.3.6 (Release date: 2017-05-16)

NEW FEATURES

  • Added plots showing expected type 1 error given an estimated empirical null distribution.

  • Closed form solution for RR vs SE plot for faster computation (especially when plotting credible intervals).

BUG FIXES

  • Several improvements of the robustness of fitting systematic error models.

EmpiricalCalibration v1.3.1 (Release date: 2017-05-16)

NEW FEATURES

  • Confidence interval calibration model StdDev transformed to log scale to prevent negative StdDev.

  • Confidence interval calibration cross-validation now allows specification of leave-out groups.

  • various new plots for evaluating confidence interval calibration.

  • Added confidence interval calibration vignette.

  • Added example data for confidence interval calibration

BUG FIXES

  • None

EmpiricalCalibration v1.2.0 (Release date: 2016-08-15)

NEW FEATURES

  • Ability to add credible intervals to calibration effect plot

  • Plot CI calibration (using leave-one-out cross-validation)

BUG FIXES

  • Fixed vignette name in index

  • Removed coverage plot (moved to MethodEvaluation package)

EmpiricalCalibration v1.1.0 (Release date: 2016-02-15)

Changes: initial submission to CRAN

Reference manual

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install.packages("EmpiricalCalibration")

1.3.6 by Martijn Schuemie, 6 months ago


https://github.com/OHDSI/EmpiricalCalibration


Report a bug at https://github.com/OHDSI/EmpiricalCalibration/issues


Browse source code at https://github.com/cran/EmpiricalCalibration


Authors: Martijn Schuemie, Marc Suchard


Documentation:   PDF Manual  


Apache License 2.0 license


Imports ggplot2, gridExtra, methods

Suggests knitr, rmarkdown


See at CRAN