tipr (0.1.1)

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Tipping Point Analyses.

http://cran.r-project.org/web/packages/tipr

The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, a plausible residual effect size for an unmeasured continuous or binary confounder, and a realistic mean difference or prevalence difference for this hypothetical confounder. Building on the methods put forth by Lin, Psaty, & Kronmal (1998) , we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance, rendering the study inconclusive.

Maintainer: Lucy D'Agostino McGowan
Author(s): Lucy D'Agostino McGowan

License: MIT + file LICENSE

Uses: broom, purrr, tibble, testthat

Released over 1 year ago.


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