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sensitivitymv:Sensitivity Analysis in Observational Studies
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>.
Maintained by Paul R. Rosenbaum. Last updated 7 years ago.
2.08 score 4 dependentscran
sensitivitymult:Sensitivity Analysis for Observational Studies with Multiple Outcomes
Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>. Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.
Maintained by Paul R. Rosenbaum. Last updated 8 years ago.
1.95 score 3 dependentscran
sensitivitymw:Sensitivity Analysis for Observational Studies Using Weighted M-Statistics
Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test). The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 <doi:10.1080/01621459.2013.879261>.
Maintained by Paul R. Rosenbaum. Last updated 3 years ago.
1 stars 1.00 scorecran
submax:Effect Modification in Observational Studies Using the Submax Method
Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates. The submax or subgroup-maximum method of Lee et al. (2017) <arXiv:1702.00525> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.
Maintained by Paul R. Rosenbaum. Last updated 7 years ago.
1.00 score