e_value

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

e_value function

<dl><dt>effect_observed</dt>
<dd>Numeric positive value. Observed exposure - outcome effect
(assumed to be the exponentiated coefficient, so a risk ratio, odds
ratio, or hazard ratio). This can be the point estimate, lower confidence
bound, or upper confidence bound.</dd></dl>

Arguments

Calculate an E-value — e_value

<dl>

<dt>effect_observed</dt>
<dd>Numeric positive value. Observed exposure - outcome effect
(assumed to be the exponentiated coefficient, so a risk ratio, odds
ratio, or hazard ratio). This can be the point estimate, lower confidence
bound, or upper confidence bound.</dd>

</dl>

e_value: Calculate an E-value

Description

Usage

Value

Arguments

Examples