r_value

This function wraps the <code>sensemakr::robustness_value()</code> function

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

r_value function

<dl><dt>effect_observed</dt>
<dd>Numeric. Observed exposure - outcome effect from a
regression model. This is the point estimate (beta coefficient)</dd>
<dt>se</dt>
<dd>Numeric. Standard error of the <code>effect_observed</code> in the previous parameter.</dd>
<dt>df</dt>
<dd>Numeric positive value. Residual degrees of freedom for the model
used to estimate the observed exposure - outcome effect. This is the total
number of observations minus the number of parameters estimated in your
model. Often for models estimated with an intercept this is N - k - 1
where k is the number of predictors in the model.</dd>
<dt>...</dt>
<dd>Optional arguments passed to the <code>sensemakr::robustness_value()</code>
function.</dd></dl>

Arguments

Robustness value — r_value

<dl>

<dt>effect_observed</dt>
<dd>Numeric. Observed exposure - outcome effect from a
regression model. This is the point estimate (beta coefficient)</dd>


<dt>se</dt>
<dd>Numeric. Standard error of the <code>effect_observed</code> in the previous parameter.</dd>


<dt>df</dt>
<dd>Numeric positive value. Residual degrees of freedom for the model
used to estimate the observed exposure - outcome effect. This is the total
number of observations minus the number of parameters estimated in your
model. Often for models estimated with an intercept this is N - k - 1
where k is the number of predictors in the model.</dd>


<dt>...</dt>
<dd>Optional arguments passed to the <code>sensemakr::robustness_value()</code>
function.</dd>

</dl>

r_value: Robustness value

Description

Usage

Value

Arguments

References

Examples