exdata_continuous

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_continuous 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_continuous: Example Data (Continuous Outcome)

Description

Usage

Arguments

Format