tip_rr_with_binary

Choose two of the following three to specify, and the third will be estimated:<ul>
<li><code>exposed_confounder_prev</code></li>
<li><code>unexposed_confounder_prev</code></li>
<li><code>confounder_outcome_effect</code></li>
</ul>Alternatively, specify all three and the function will return the number of unmeasured
confounders specified needed to tip the analysis.

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

tip_rr_with_binary function

<dl><dt>effect_observed</dt>
<dd>Numeric positive value. Observed exposure - outcome risk ratio.
This can be the point estimate, lower confidence bound, or upper
confidence bound.</dd>
<dt>exposed_confounder_prev</dt>
<dd>Numeric between 0 and 1. Estimated prevalence of the
unmeasured confounder in the exposed population</dd>
<dt>unexposed_confounder_prev</dt>
<dd>Numeric between 0 and 1. Estimated prevalence of the
unmeasured confounder in the unexposed population</dd>
<dt>confounder_outcome_effect</dt>
<dd>Numeric positive value. Estimated relationship
between the unmeasured confounder and the outcome</dd>
<dt>verbose</dt>
<dd>Logical. Indicates whether to print informative message.
Default: <code>TRUE</code></dd></dl>

Arguments

Choose two of the following three to specify, and the third will be estimated:<ul>
<li><code>exposed_confounder_prev</code></li>
<li><code>unexposed_confounder_prev</code></li>
<li><code>confounder_outcome_effect</code></li>
</ul>

Alternatively, specify all three and the function will return the number of unmeasured
confounders specified needed to tip the analysis.

Tip an observed risk ratio with a binary confounder. — tip_rr_with_binary

<dl>

<dt>effect_observed</dt>
<dd>Numeric positive value. Observed exposure - outcome risk ratio.
This can be the point estimate, lower confidence bound, or upper
confidence bound.</dd>


<dt>exposed_confounder_prev</dt>
<dd>Numeric between 0 and 1. Estimated prevalence of the
unmeasured confounder in the exposed population</dd>


<dt>unexposed_confounder_prev</dt>
<dd>Numeric between 0 and 1. Estimated prevalence of the
unmeasured confounder in the unexposed population</dd>


<dt>confounder_outcome_effect</dt>
<dd>Numeric positive value. Estimated relationship
between the unmeasured confounder and the outcome</dd>


<dt>verbose</dt>
<dd>Logical. Indicates whether to print informative message.
Default: <code>TRUE</code></dd>

</dl>

tip_rr_with_binary: Tip an observed risk ratio with a binary confounder.

Description

Usage

Arguments