riskCommunicator: riskCommunicator: Obtaining interpretable epidemiological...

In riskCommunicator: G-Computation to Estimate Interpretable Epidemiological Effects

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.

See Also

gComp

pointEstimate

plot.gComp

riskCommunicator documentation built on June 1, 2022, 1:07 a.m.