dstat-package: Conditional Sensitivity Analysis for Matched Observational...

In dstat: Conditional Sensitivity Analysis for Matched Observational Studies

Description

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>.

Details

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The package provides a sensitivity analysis for a conditional test of the null hypothesis of no treatment effect in a matched observational study in which the unmeasured bias in treatment assignment is quantified by a sensitivity parameter gamma>=1. The test uses only those categories of pairs that demonstrate insensitivity to a bias of magnitude gamma, correcting for data-dependent selection of categories by conditional inference. The main function in the package is dstat().

Author(s)

Paul R. Rosenbaum

Maintainer: Paul R. Rosenbaum <[e-mail]>

References

Rosenbaum, P. R. (1999). Using quantile averages in matched observational studies. Journal of the Royal Statistical Society: Series C (Applied Statistics), 48(1), 63-78. <doi.org/10.1111/1467-9876.00140>

Examples

data("dental")
attach(dental)
head(dental)
dstat(y,gamma=4.1,f=dose:age,fscore=c(1,1,2,2))
amplify(4,c(5,6,7))
detach(dental)

dstat documentation built on May 2, 2019, 9:27 a.m.