| 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 (2024) <doi:10.1080/01621459.2023.2221402>. The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>. The main functions are wgtRank(), wgtRankCI() and wgtRanktt().
Details
The DESCRIPTION file:
| Package: | weightedRank |
| Type: | Package |
| Title: | Sensitivity Analysis Using Weighted Rank Statistics |
| Version: | 0.3.7 |
| 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 (2024) <doi:10.1080/01621459.2023.2221402>. The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>. The main functions are wgtRank(), wgtRankCI() and wgtRanktt(). |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | stats, graphics, mvtnorm, sensitivitymv, senstrat |
| 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
aHDLe HDL Cholesterol and Light Daily Alcohol
2013-2020
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
gwgtRank Generalized Sensitivity Analysis for Weighted
Rank Statistics in Block Designs
stepSolve Root of a Monotone Decreasing Step Function
weightedRank-package Sensitivity Analysis Using Weighted Rank
Statistics
wgtRank Sensitivity Analysis for Weighted Rank
Statistics in Block Designs
wgtRankC Sensitivity Analysis for a Conditional Weighted
Rank Test
wgtRankCI Sensitivity Analysis for Confidence Intervals
and Point Estimates from 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 wgtRankCI() inverts the test to obtain confidence intervals and Hodges-Lehmann point estimates. 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 U‐Statistic 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. (2023) <doi:10.1111/biom.13921> A second evidence factor for a second control group. Biometrics, 79(4), 3968-3980.
Rosenbaum, P. R. (2024) <doi:10.1080/01621459.2023.2221402> Bahadur efficiency of observational block designs. Journal of the American Statistical Association.
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)