| sensitivitymv-package {sensitivitymv} | R Documentation |
Sensitivity Analysis in Observational Studies
Description
The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.
Details
The DESCRIPTION file:
| Package: | sensitivitymv |
| Type: | Package |
| Title: | Sensitivity Analysis in Observational Studies |
| Version: | 1.4.3 |
| Author: | Paul R. Rosenbaum |
| Maintainer: | Paul R. Rosenbaum <rosenbaum@wharton.upenn.edu> |
| Description: | The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
Index of help topics:
amplify Amplification of sensitivity analysis in
observational studies.
erpcp DNA Damage Among Welders
lead150 Smoking and lead in 150 matched 1-5 sets.
lead250 Smoking and lead in 250 matched pairs.
mercury NHANES Mercury/Fish Data
mscorev Computes the M-scores used by senmv.
mtm DNA damage from exposure to chromium
multrnks Approximate scores for ranks.
newurks Approximate scores for ranks of row ranges.
senmv Sensitivity analysis in observational studies
using Huber-Maritz M-statistics.
sensitivitymv-package Sensitivity Analysis in Observational Studies
separable1v Asymptotic separable calculations internal to
other functions.
tbmetaphase Genetic damage from drugs used to treat TB
truncatedP Trucated product of P-values.
truncatedPbg Trucated product of P-values using the mixture
formula.
Author(s)
Paul R. Rosenbaum
Maintainer: Paul R. Rosenbaum <rosenbaum@wharton.upenn.edu>
References
Rosenbaum, P. R. (2007) Sensitivity analysis for m-estimates, tests and confidence intervals in matched observational studies. Biometrics, 2007, 63, 456-464. <doi:10.1111/j.1541-0420.2006.00717.x>
Rosenbaum, P. R. (2010) Design of Observational Studies. New York: Springer <doi:10.1007/978-1-4419-1213-8> Section 2.9 explains randomization inference with M-statistics in an example with 5 matched pairs.
Rosenbaum, P. R. (2013) Impact of multiple matched controls on design sensitivity in observational studies. Biometrics, 2013, 69, 118-127. <doi:10.1111/j.1541-0420.2012.01821.x>
Rosenbaum, P. R. (2015). Two R packages for sensitivity analysis in observational studies. Observational Studies, 1(1), 1-17. Free on-line at obsstudies.org
Rosenbaum, P. R. (2017) Observation and Experiment: An Introduction to Causal Inference. Cambridge, MA: Harvard University Press. Chapter 9 discusses sensitivity analysis.
Examples
# Example reproduces parts of sect. 4.3 in Rosenbaum (2007)
data(tbmetaphase)
senmv(tbmetaphase,gamma=2,trim=1)