adjust_coef_with_binary

Adjust an observed coefficient from a loglinear model with a binary confounder

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

adjust_coef_with_binary function

<dl><dt>effect_observed</dt>
<dd>Numeric. Observed exposure - outcome effect from a loglinear
model. This can be the beta coefficient, the lower confidence bound of
the beta coefficient, or the upper confidence bound of the beta
coefficient.</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. 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

Adjust an observed coefficient from a loglinear model with a binary confounder — adjust_coef_with_binary

<dl>

<dt>effect_observed</dt>
<dd>Numeric. Observed exposure - outcome effect from a loglinear
model. This can be the beta coefficient, the lower confidence bound of
the beta coefficient, or the upper confidence bound of the beta
coefficient.</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. 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>

adjust_coef_with_binary: Adjust an observed coefficient from a loglinear model with a binary confounder

Description

Usage

Value

Arguments

Examples