No packages match

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>.

Last updated

2.98 score 5 dependents 64 scripts 343 downloads

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.

Last updated

2.57 score 3 dependents 41 scripts 206 downloads

senstrat - Sensitivity Analysis for Stratified Observational Studies

Sensitivity analysis in unmatched observational studies, with or without strata. The main functions are sen2sample() and senstrat(). See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 <doi:10.1111/1467-9868.00249> .

Last updated

2.51 score 3 dependents 36 scripts 274 downloads

DOS2 - Design of Observational Studies, Companion to the Second Edition

Contains data sets, examples and software from the Second Edition of "Design of Observational Studies"; see Rosenbaum, P.R. (2010) <doi:10.1007/978-1-4419-1213-8>.

Last updated

2.35 score 2 stars 1 dependents 37 scripts 233 downloads

DOS - Design of Observational Studies

Contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.

Last updated

2.28 score 19 scripts 192 downloads

sensitivity2x2xk - Sensitivity Analysis for 2x2xk Tables in Observational Studies

Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.

Last updated

2.01 score 2 dependents 17 scripts 213 downloads

sensitivityfull - Sensitivity Analysis for Full Matching in Observational Studies

Sensitivity to unmeasured biases in an observational study that is a full match. Function senfm() performs tests and function senfmCI() creates confidence intervals. The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.

Last updated

1.88 score 1 dependents 25 scripts 618 downloads

iTOS - Methods and Examples from Introduction to the Theory of Observational Studies

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'.

Last updated

1.78 score 2 dependents 6 scripts 221 downloads

fugue - Sensitivity Analysis Optimized for Matched Sets of Varied Sizes

As in music, a fugue statistic repeats a theme in small variations. Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes. The main functions in the package are sen() and senCI(). For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.

Last updated

1.70 score 4 scripts 142 downloads

weightedRank - Sensitivity Analysis Using Weighted Rank Statistics

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 package can increase design sensitivity using the conditioning tactic in Rosenbaum (2025) <doi:10.1093/jrsssb/qkaf007>. The main functions are wgtRank(), wgtRankCI(), wgtRanktt() and wgtRankC().

Last updated

1.60 score 2 stars 541 downloads

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>.

Last updated

1.60 score 1 stars 40 scripts 319 downloads

alcoholSurv - Light Daily Alcohol and Longevity

Contains data from an observational study concerning possible effects of light daily alcohol consumption on survival and on HDL cholesterol. It also replicates various simple analyses in Rosenbaum (2025) <doi:10.1080/09332480.2025.2473291>. Finally, it includes new R code in wgtRankCef() that implements and replicates a new method for constructing evidence factors in observational block designs in Rosenbaum (2026) <doi:10.1080/01621459.2026.2624858>).

Last updated

1.30 score 309 downloads

aamatch - Artless Automatic or Artful Multivariate Matching for Observational Studies

Implements a simple version of multivariate matching using a propensity score, near-exact matching, near-fine balance, and robust Mahalanobis distance matching (Rosenbaum 2020 <doi:10.1146/annurev-statistics-031219-041058>). You specify the variables, and the program does everything else.

Last updated

1.30 score 1 scripts 206 downloads

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. (2018) <doi:10.1111/biom.12884> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.

Last updated

1.00 score 9 scripts 171 downloads

tailTransform - Symmetric Transformation of Tails for Plotting Differences

When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting. The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2022) <doi:10.1080/00031305.2022.2063944>.

Last updated

1.00 score 295 downloads

tightenBlock - Tightens an Observational Block Design by Balanced Subset Matching

Tightens an observational block design into a smaller design with either smaller or fewer blocks while controlling for covariates. The method uses fine balance, optimal subset matching (Rosenbaum, 2012 <doi:10.1198/jcgs.2011.09219>) and two-criteria matching (Zhang et al 2023 <doi:10.1080/01621459.2021.1981337>). The main function is tighten(). The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) <doi:10.1007/BF02288322>, (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at <https://github.com/josherrickson/rrelaxiv/>, (iv) is not essential to use the package.

Last updated

1.00 score 172 downloads

dstat2x2xk - Demonstrated Insensitivity to Bias in 2x2xK Contingency Tables

For an observational study with binary treatment, binary outcome and K strata, implements a d-statistic that uses those strata most insensitive to unmeasured bias in treatment assignment.<doi:10.1093/biomet/asaa032> The package has one function, dstat2x2xk.

Last updated

1.00 score 201 downloads

evident - Evidence Factors in Observational Studies

Contains a collection of examples of evidence factors in observational studies from the book Replication and Evidence Factors in Observational Studies by Paul R. Rosenbaum (2021) <doi:10.1201/9781003039648>.

Last updated

1.00 score 1 scripts 203 downloads

informedSen - Sensitivity Analysis Informed by a Test for Bias

After testing for biased treatment assignment in an observational study using an unaffected outcome, the sensitivity analysis is constrained to be compatible with that test. The package uses the optimization software gurobi obtainable from <https://www.gurobi.com/>, together with its associated R package, also called gurobi; see: <https://www.gurobi.com/documentation/7.0/refman/installing_the_r_package.html>. The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374 <doi:10.1093/biomet/79.2.367>.

Last updated

1.00 score 208 downloads

testtwice - Testing One Hypothesis Twice in Observational Studies

Tests one hypothesis with several test statistics, correcting for multiple testing. The central function in the package is testtwice(). In a sensitivity analysis, the method has the largest design sensitivity of its component tests. The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.

Last updated

1.00 score 176 downloads

dstat - Conditional Sensitivity Analysis for Matched Observational Studies

A d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments. A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>). There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>.

Last updated

1.00 score 222 downloads

exteriorMatch - Constructs the Exterior Match from Two Matched Control Groups

If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups. The exterior match is used to compare the two control groups.

Last updated

1.00 score 2 scripts 144 downloads