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Package: tipr | ||
Type: Package | ||
Title: Tipping Point Analyses | ||
Version: 1.0.0 | ||
Version: 1.0.1 | ||
Authors@R: c( | ||
person("Lucy", "D'Agostino McGowan", , "[email protected]", | ||
role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6983-2759")) | ||
) | ||
Description: 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. | ||
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.<doi:10.21105/joss.04495> | ||
License: MIT + file LICENSE | ||
Encoding: UTF-8 | ||
RoxygenNote: 7.1.2 | ||
|
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bibentry( | ||
"Article", | ||
title = "tipr: An R package for sensitivity analyses for unmeasured confounders", | ||
author = "Lucy D'Agostino McGowan", | ||
year = 2022, | ||
journal = "Journal of Open Source Software", | ||
volume = 7, | ||
number = 77, | ||
pages = 4495, | ||
doi = "10.21105/joss.04495" | ||
) |