exdata_rr

A data set simulated with two Normally distributed confounders, one
"measured" and one "unmeasured", an exposure, and outcome. The "true" causal
effect of the exposure on the outcome, accounting for both the measured
and unmeasured confounders, should be 0.

datasets

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.

Lucy D'Agostino McGowan

tipr

Tipping Point Analyses

exdata_rr function

A data frame with 2,000 rows and 4 columns:<ul>
<li><code>.unmeasured_confounder</code>: A simulated unmeasured confounder</li>
<li><code>measured_confounder</code>: A simulated measured confounder</li>
<li><code>exposure</code></li>
<li><code>outcome</code></li>
</ul>

exdata_rr: Example Data (Risk Ratio)

Description

Usage

Arguments

Format