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 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. (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)

[Package weightedRank version 0.2.5 Index]