Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Sci Rep. 2018 Jun 18;8(1):9274. doi: 10.1038/s41598-018-27660-2.
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) test 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 the information of disease prevalence to estimate the perils of particular diseases. Moreover, we adopted the PRISM test to assess the sufficient-cause interaction in case-control studies for non-rare diseases. The Monte Carlo simulation showed that our proposed method can maintain reasonably accurate type I error rates in all situations. Its powers are comparable to the odds-scale PRISM test and far greater than the risk-scale RERI test and the odds-scale RERI test. 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.
充分病因交互作用（也称为机制交互作用或因果协同作用）最近受到了相当多的关注。两个统计测试，即“交互作用的相对超额风险”（RERI）测试和“基于乘法性的协同增效危险比指标”（PRISM）测试，专门用于检验队列研究中的这种交互作用。此外，这两种测试可应用于罕见疾病的病例对照研究，但不适用于非罕见疾病。在这项研究中，我们提出了一种方法，将疾病流行率的信息纳入其中，以估计特定疾病的危险。此外，我们采用 PRISM 测试来评估非罕见疾病的病例对照研究中的充分病因交互作用。蒙特卡罗模拟表明，我们提出的方法在所有情况下都能保持合理准确的Ⅰ类错误率。其功效与赔率尺度 PRISM 测试相当，远大于风险尺度 RERI 测试和赔率尺度 RERI 测试。鉴于其理想的统计性质，我们建议在病例对照研究中使用所提出的方法来检验两个二项暴露之间的充分病因交互作用。
