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.] (http://dx.doi.org/10.1002/sim.5925).

Features

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

#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
[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.