EmpiricalCalibration is part of the OHDSI Methods Library.
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.
Also supported is empirical calibration of confidence intervals, based on the results for a set of negative and positive controls, as described in the paper Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data.
data(sccs) #Load one of the included data sets negatives <- sccs[sccs$groundTruth == 0,] #Select the negative controls null <- fitNull(logRr = negatives$logRr, seLogRr = negatives$seLogRr) #Fit the null distribution positive <- sccs[sccs$groundTruth == 1,] #Select the positive control #Create the plot above: plotCalibrationEffect(logRrNegatives = negatives$logRr, seLogRrNegatives = negatives$seLogRr, logRrPositives = positive$logRr, seLogRrPositives = positive$seLogRr, null = null) #Compute the calibrated p-value: calibrateP(null = null, logRr = positive$logRr, seLogRr = positive$seLogRr) #Compute calibrated p-value  0.8390598
In R, use the following commands to install the latest stable version from CRAN:
To install the latest development version directly from GitHub, use:
This package has been developed in RStudio.