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, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance.

Getting started

Package details

AuthorLucy D'Agostino McGowan [aut, cre] (<https://orcid.org/0000-0002-6983-2759>)
MaintainerLucy D'Agostino McGowan <lucydagostino@gmail.com>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

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tipr documentation built on Sept. 5, 2022, 5:09 p.m.