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.

Getting started

Package details

AuthorLucy D'Agostino McGowan
MaintainerLucy D'Agostino McGowan <[email protected]>
LicenseMIT + file LICENSE
Version0.1.1
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("tipr")

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tipr documentation built on May 2, 2019, 1:46 p.m.