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.
