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: 0.2.0
Imports: glue, tibble, purrr
Suggests: testthat, broom, dplyr, MASS
Published: 2020-11-16
Author: Lucy D'Agostino McGowan
Maintainer: Lucy D'Agostino McGowan <lucydagostino at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: tipr results

Downloads:

Reference manual: tipr.pdf
Package source: tipr_0.2.0.tar.gz
Windows binaries: r-devel: tipr_0.2.0.zip, r-release: tipr_0.2.0.zip, r-oldrel: tipr_0.2.0.zip
macOS binaries: r-release (arm64): tipr_0.2.0.tgz, r-release (x86_64): tipr_0.2.0.tgz, r-oldrel: tipr_0.2.0.tgz
Old sources: tipr archive

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