| riskCommunicator {riskCommunicator} | R Documentation |
riskCommunicator: Obtaining interpretable epidemiological effect estimates
Description
riskCommunicator is a package for estimating flexible epidemiological
effect measures including both differences and ratios. The package is based
on the parametric G-formula (g-computation with parametric models) developed
by Robbins et. al. in 1986 as an alternative to inverse probability weighting.
It is useful for estimating the impact of interventions in the presence of
treatment-confounder-feedback and is a powerful tool for causal inference,
but has seen limited success due to lack of software for the computationally
intensive components. This package provides three main functions.
The first, pointEstimate, obtains a point estimate of the difference
and ratio effect estimates. This function is typically called within the
gComp function, but is available for use in special cases for example
when the user requires more explicit control over bootstrap resampling
(e.g. nested clusters). The second function, gComp, is the workhorse
function that obtains point estimates for difference and ratio effects along
with their 95/
to visualize the bootstrap results. We provide the framingham dataset,
which is the teaching dataset from the Framingham Heart Study, as well as a
subset of that data, cvdd for users.
References
Robins, James. 1986. “A New Approach To Causal Inference in Mortality Studies with a Sustained Exposure Period - Application To Control of the Healthy Worker Survivor Effect.” Mathematical Modelling 7: 1393–1512. doi:10.1016/0270-0255(86)90088-6.