Epidemiologists often need to determine how two exposures interact to put subjects at a higher risk of experiencing an outcome of interest. For example, you might want to investigate how exposures to cigarete smoke and asbestos interact to increase the risk of lung cancer. With the new reri command,
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you can measure two-way interactions in an additive model of relative risk, while accounting for other risk factors.
Researchers can choose from various supported models, such as logistic, binomial generalized linear, Poisson, negative binomial, Cox, parametric survival, interval-censored parametric survival, and interval-censored Cox models. They can evaluate an additive model for the interaction of smoke and asbestos by using three related statistics: RERI, atributable proportion, and synergy index.