| informedSen-package {informedSen} | R Documentation |
Sensitivity Analysis Informed by a Test for Bias
Description
After testing for biased treatment assignment in an observational study using an unaffected outcome, the sensitivity analysis is constrained to be compatible with that test. The package uses the optimization software gurobi obtainable from <https://www.gurobi.com/>, together with its associated R package, also called gurobi; see: <https://www.gurobi.com/documentation/7.0/refman/installing_the_r_package.html>. The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374 <doi:10.1093/biomet/79.2.367>.
Details
The DESCRIPTION file:
| Package: | informedSen |
| Type: | Package |
| Title: | Sensitivity Analysis Informed by a Test for Bias |
| Version: | 1.0.7 |
| Author: | Paul R Rosenbaum |
| Maintainer: | Paul R Rosenbaum <rosenbaum@wharton.upenn.edu> |
| Description: | After testing for biased treatment assignment in an observational study using an unaffected outcome, the sensitivity analysis is constrained to be compatible with that test. The package uses the optimization software gurobi obtainable from <https://www.gurobi.com/>, together with its associated R package, also called gurobi; see: <https://www.gurobi.com/documentation/7.0/refman/installing_the_r_package.html>. The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374 <doi:10.1093/biomet/79.2.367>. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | sensitivitymult, stats |
| Enhances: | gurobi |
| Depends: | R (>= 3.5.0) |
Index of help topics:
HDL Light Daily Alcohol and HDL Cholesterol Levels
informedSen-package Sensitivity Analysis Informed by a Test for
Bias
informedsen Sensitivity Analysis Informed by a Test for
Unmeasured Bias
senmscores Computes M-scores for M-tests.
The package performs a sensitivity analysis within a confidence set provided by a test for unmeasured bias. The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies. Biometrika, 79(2), 367-374. <doi:10.1093/biomet/79.2.367> The main function in the package is informedsen. The package uses the optimization software gurobi obtainable from <https://www.gurobi.com/>, together with its associated R package, also called gurobi; see: <https://www.gurobi.com/documentation/7.0/refman/installing_the_r_package.html>. See the example in informedsen for discussion about obtaining gurobi and its associated local R-package.
Author(s)
Paul R Rosenbaum
Maintainer: Paul R Rosenbaum <rosenbaum@wharton.upenn.edu>
References
Rosenbaum, P. R. (1984). From association to causation in observational studies: The role of tests of strongly ignorable treatment assignment. Journal of the American Statistical Association 79, 41-48. <doi:10.1080/01621459.1984.10477060>
Rosenbaum, P. R. (1989a). On permutation tests for hidden biases in observational studies. The Annals of Statistics 17, 643-653. <doi:10.1214/aos/1176347131>
Rosenbaum, P. R. (1989b). The role of known effects in observational studies. Biometrics 45, 557-569. <doi:10.2307/2531497>
Rosenbaum, P. R. (1992). Detecting bias with confidence in observational studies. Biometrika, 79(2), 367-374. <doi:10.1093/biomet/79.2.367>
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. (2021). Sensitivity analyses informed by tests for bias in observational studies. Manuscript. This manuscript describes and illustrates the new computational tools that make feasible the method in Rosenbaum (1992). The example in the package is the example in this manuscript.
Examples
## Not run:
# To run these examples, you MUST have gurobi installed.
# The makers of gurobi provide free access to academics.
# Additionally, you must install the local R package gurobi
# that is provided by installing gurobi.
# The examples are from Rosenbaum (2021)
data(HDL)
shdl<-senmscores(HDL$hdl,HDL$z,HDL$mset)
smmerc<-senmscores(HDL$mmercury,HDL$z,HDL$mset)
sc<-cbind(shdl,smmerc)
informedsen(3.5, sc, HDL$z, HDL$mset, alpha = 0.05)
informedsen(3.4, sc, HDL$z, HDL$mset, alpha = c(0.04,0.01))
## End(Not run)