observed_bias_order

Order observed bias data frame for plotting

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

observed_bias_order function

<dl><dt>d</dt>
<dd>Observed bias data frame. Must have columns <code>dropped</code> and <code>type</code></dd>
<dt>by</dt>
<dd>Character. Variable in <code>d</code> to order by.</dd></dl>

Arguments

Order observed bias data frame for plotting — observed_bias_order

<dl>

<dt>d</dt>
<dd>Observed bias data frame. Must have columns <code>dropped</code> and <code>type</code></dd>


<dt>by</dt>
<dd>Character. Variable in <code>d</code> to order by.</dd>

</dl>

observed_bias_order: Order observed bias data frame for plotting

Description

Usage

Value

Arguments