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 + 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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=tipr to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4