EmpiricalCalibration is part of HADES.
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 <- sccs[sccs$groundTruth == 0,] #Select the negative controls negatives <- fitNull(logRr = negatives$logRr, seLogRr = negatives$seLogRr) #Fit the null distribution null <- sccs[sccs$groundTruth == 1,] #Select the positive control positive #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 1] 0.8390598[
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