weightedRank-package {weightedRank} | R Documentation |
Sensitivity Analysis Using Weighted Rank Statistics
Description
Performs a sensitivity analysis using weighted rank tests in observational studies with I blocks of size J; see Rosenbaum (2018) <doi:10.1214/18-AOAS1153>. The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>. The main functions are wgtRank() and wgtRanktt() and ef2C().
Details
The DESCRIPTION file:
Package: | weightedRank |
Type: | Package |
Title: | Sensitivity Analysis Using Weighted Rank Statistics |
Version: | 0.2.5 |
Authors@R: | person("Paul", "Rosenbaum", email = "rosenbaum@wharton.upenn.edu", role = c("aut", "cre")) |
Description: | Performs a sensitivity analysis using weighted rank tests in observational studies with I blocks of size J; see Rosenbaum (2018) <doi:10.1214/18-AOAS1153>. The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>. The main functions are wgtRank() and wgtRanktt() and ef2C(). |
License: | GPL-2 |
Encoding: | UTF-8 |
LazyData: | true |
Imports: | stats, graphics, mvtnorm, sensitivitymv |
Suggests: | sensitivitymw, sensitivitymult, DOS2 |
Depends: | R (>= 3.5.0) |
Author: | Paul Rosenbaum [aut, cre] |
Maintainer: | Paul Rosenbaum <rosenbaum@wharton.upenn.edu> |
Index of help topics:
aBP Binge Drinking and Blood Pressure aHDL Alcohol and HDL Cholesterol amplify Amplification of sensitivity analysis in observational studies. dwgtRank Weighted Rank Statistics for Evidence Factors with Two Control Groups ef2C Evidence Factors For Matched Triples With Two Control Groups weightedRank-package Sensitivity Analysis Using Weighted Rank Statistics wgtRank Sensitivity Analysis for Weighted Rank Statistics in Block Designs wgtRanktt Adaptive Inference Using Two Test Statistics in a Block Design
The package conducts either fixed or adaptive sensitivity analyses for observational studies with I blocks and J individuals in each block, one treated and J-1 controls. The two main functions are wgtRank() for a fixed test statistic, and wgtRanktt() for an adaptive choice of one of two test statistics. The function ef2C() is used to extract two evidence factors when a treated group is compared to two different control groups.
Author(s)
NA
Maintainer: NA
References
Berk, R. H. and Jones, D. H. (1978) <https://www.jstor.org/stable/4615706> Relatively optimal combinations of test statistics. Scandinavian Journal of Statistics, 5, 158-162.
Quade, D. (1979) <doi:10.2307/2286991> Using weighted rankings in the analysis of complete blocks with additive block effects. Journal of the American Statistical Association, 74, 680-683.
Rosenbaum, P. R. (1987). <doi:10.1214/ss/1177013232> The role of a second control group in an observational study. Statistical Science, 2, 292-306.
Rosenbaum, P. R. (2011) <doi:10.1111/j.1541-0420.2010.01535.x> A new UāStatistic with superior design sensitivity in matched observational studies. Biometrics, 67(3), 1017-1027.
Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.
Rosenbaum, P. R. (2021) <doi:10.1201/9781003039648> Replication and Evidence Factors in Observational Studies. Chapman and Hall/CRC.
Rosenbaum, P. R. (2022) Bahadur efficiency of observational block designs. Manuscript.
Tardif, S. (1987) <doi:10.2307/2289476> Efficiency and optimality results for tests based on weighted rankings. Journal of the American Statistical Association, 82(398), 637-644.
Examples
data(aHDL)
y<-t(matrix(aHDL$hdl,4,406))
wgtRank(y,phi="u878",gamma=6) # New U-statistic weights (8,7,8)
wgtRanktt(y,phi1="u868",phi2="u878",gamma=5.9)