The assessment of interactions is a critical issue in epidemiology. Conducted by Mr. Jui-Hsiang Lin, a doctoral student (now a research assistant professor) at National Taiwan University, and his advisor Dr. Wen-Chung Lee, professor in the Institute of Epidemiology and Preventive Medicine in the College of Public Health, this study proposed a novel method to test sufficient-cause interactions in case-control studies of non-rare diseases. The study was published on June 18 in Scientific Reports.
Sufficient-cause interaction (also called mechanistic interaction or causal co-action) has received considerable attention recently. Two statistical tests, the ‘relative excess risk due to interaction’ (RERI) and the ‘peril ratio index of synergy based on multiplicativity’ (PRISM) test, were developed specifically to test such an interaction in cohort studies. In addition, these two tests can be applied in case-control studies for rare diseases but are not valid for non-rare diseases. In this study, we proposed a method to incorporate information regarding disease prevalence to estimate disease perils, and then adopted the PRISM test to assess the sufficient-cause interaction in case-control studies. In our method, only the disease prevalence of the population at large is required which is readily available from vital statistics or previously published studies. The Monte-Carlo simulation showed that the proposed method can maintain reasonably accurate type I error rates in rare diseases and non-rare diseases. Its powers are comparable to the PRISM test in rare diseases and far greater than the RERI test estimating similar to the proposed method in non-rare diseases.
In light of its desirable statistical properties, we recommend using the proposed method to test for sufficient-cause interactions between two binary exposures in case-control studies.