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.
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