iTOS-package {iTOS}R Documentation

Methods and Examples from Introduction to the Theory of Observational Studies

Description

Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are 'aHDL' from Rosenbaum (2023a) <doi:10.1111/biom.13558> and 'bingeM' from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data 'bingeM' from 'binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'.

Details

The DESCRIPTION file:

Package: iTOS
Type: Package
Title: Methods and Examples from Introduction to the Theory of Observational Studies
Version: 1.0.3
Authors@R: person(given = c("Paul", "R."), family = "Rosenbaum", role = c("aut", "cre"), email = "rosenbaum@wharton.upenn.edu")
Author: Paul R. Rosenbaum [aut, cre]
Maintainer: Paul R. Rosenbaum <rosenbaum@wharton.upenn.edu>
Description: Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are 'aHDL' from Rosenbaum (2023a) <doi:10.1111/biom.13558> and 'bingeM' from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data 'bingeM' from 'binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'.
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: stats, MASS, rcbalance, BiasedUrn, xtable
Suggests: weightedRank
Depends: R (>= 3.5.0)

Index of help topics:

aHDL                    Alcohol and HDL Cholesterol
addMahal                Rank-Based Mahalanobis Distance Matrix
addNearExact            Add a Near-exact Penalty to an Exisiting
                        Distance Matrix.
addcaliper              Add a Caliper to an Existing Cost Matrix
addinteger              Add an Integer Penalty to an Existing Distance
                        Matrix
addquantile             Cut a Covariate at Quantiles and Add a Penalty
                        for Different Quantile Categories
amplify                 Amplification of sensitivity analysis in
                        observational studies.
binge                   Binge Drinking and High Blood Pressure
bingeM                  Binge Drinking and High Blood Pressure -
                        Matched With Two Control Groups
computep                Computes individual and pairwise treatment
                        assignment probabilities.
ev                      Computes the null expectation and variance for
                        one stratum.
evalBal                 Evaluate Covariate Balance in a Matched Sample
evall                   Compute expectations and variances for one
                        stratum.
gconv                   Convolution of Two Probability Generating
                        Functions
iTOS-package            Methods and Examples from Introduction to the
                        Theory of Observational Studies
makematch               Two-Criteria Matching
makenetwork             Make the Network Used for Matching with Two
                        Criteria
noether                 Sensitivity Analysis Using Noether's Test for
                        Matched Pairs
startcost               Initialize a Distance Matrix.
zeta                    zeta function in sensitivity analysis

Author(s)

Paul R. Rosenbaum [aut, cre]

Maintainer: Paul R. Rosenbaum <rosenbaum@wharton.upenn.edu>

References

Rosenbaum, Paul R. Introduction to the Theory of Observational Studies. Manuscript, 2024.

Rosenbaum, P. R. (1989) <doi:10.2307/2290079> Optimal matching for observational studies. Journal of the American Statistical Association, 84, 1024-1032.

Rosenbaum, Paul R., Richard N. Ross, and Jeffrey H. Silber (2007) <doi:10.1198/016214506000001059> Minimum distance matched sampling with fine balance in an observational study of treatment for ovarian cancer. Journal of the American Statistical Association 102, 75-83.

Rosenbaum, P. R. (2023a) <doi:10.1111/biom.13558> Sensitivity analyses informed by tests for bias in observational studies. Biometrics 79, 475-487.

Rosenbaum, P. R. (2023b) <doi:10.1111/biom.13921> A second evidence factor for a second control group. Biometrics, 79, 3968-3980.

Yang, D., Small, D. S., Silber, J. H. and Rosenbaum, P. R. (2012) <doi:10.1111/j.1541-0420.2011.01691.x> Optimal matching with minimal deviation from fine balance in a study of obesity and surgical outcomes. Biometrics, 68, 628-636.

Zhang, B., D. S. Small, K. B. Lasater, M. McHugh, J. H. Silber, and P. R. Rosenbaum (2023) <doi:10.1080/01621459.2021.1981337> Matching one sample according to two criteria in observational studies. Journal of the American Statistical Association, 118, 1140-1151.

Examples

data(binge)
table(binge$AlcGroup)
data(aHDL)
table(aHDL$grp)

[Package iTOS version 1.0.3 Index]