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.
| Version: | 1.0.2 |
| Depends: | R (≥ 2.10) |
| Imports: | cli (≥ 3.4.1),glue,purrr,rlang (≥ 1.0.6),sensemakr,tibble |
| Suggests: | broom,dplyr,MASS,testthat |
| Published: | 2024-02-06 |
| DOI: | 10.32614/CRAN.package.tipr |
| Author: | Lucy D'Agostino McGowan [aut, cre], Malcolm Barrett [aut] |
| Maintainer: | Lucy D'Agostino McGowan <lucydagostino at gmail.com> |
| BugReports: | https://github.com/r-causal/tipr/issues |
| License: | MIT + fileLICENSE |
| URL: | https://r-causal.github.io/tipr/,https://github.com/r-causal/tipr |
| NeedsCompilation: | no |
| Citation: | tipr citation info |
| Materials: | README,NEWS |
| CRAN checks: | tipr results |
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