tipr: Tipping Point Analyses

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) <doi:10.2307/2533848>, we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance, rendering the study inconclusive.

Version: 1.0.0
Depends: R (≥ 2.10)
Imports: glue, tibble, purrr, sensemakr
Suggests: testthat, broom, dplyr, MASS
Published: 2022-08-06
Author: Lucy D'Agostino McGowan ORCID iD [aut, cre]
Maintainer: Lucy D'Agostino McGowan <lucydagostino at gmail.com>
BugReports: https://github.com/LucyMcGowan/tipr/issues
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: tipr results


Reference manual: tipr.pdf


Package source: tipr_1.0.0.tar.gz
Windows binaries: r-devel: tipr_1.0.0.zip, r-release: tipr_1.0.0.zip, r-oldrel: tipr_1.0.0.zip
macOS binaries: r-release (arm64): tipr_1.0.0.tgz, r-oldrel (arm64): tipr_1.0.0.tgz, r-release (x86_64): tipr_1.0.0.tgz, r-oldrel (x86_64): tipr_1.0.0.tgz
Old sources: tipr archive


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