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>.
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Paul R. Rosenbaum
Maintainer: Paul R. Rosenbaum <[email protected]>
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.
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