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.] (http://dx.doi.org/10.1002/sim.5925).
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 #Create the plot above: 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  0.8390598
This is a pure R package.
Requires R (version 3.1.0 or newer).
In R, use the following commands to install the latest stable version from CRAN:
To install the latest development version directly from GitHub, use:
install.packages("devtools") library(devtools) install_github("ohdsi/EmpiricalCalibration")
EmpiricalCalibration is licensed under Apache License 2.0
This package has been developed in RStudio.
This package is ready for use.
Martijn Schuemie is the author of this package.