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 |
| 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 + file LICENSE |
| 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 |
Documentation:
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