Department of Epidemiology and Health Statistics, Fujian Medical University, Fuzhou, Fujian, China.
Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian, China.
PeerJ. 2024 Oct 14;12:e18304. doi: 10.7717/peerj.18304. eCollection 2024.
In epidemiology, indicators such as the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S) are commonly used to assess additive interactions between two variables. However, the results of these indicators are sometimes inconsistent in real world applications and it may be difficult to draw conclusions from them.
Based on the relationship between the RERI, AP, and S, we propose a method with consistent results, which are achieved by constraining , and the interpretation of the results is simple and clear. We present two pathways to achieve this end: one is to complete the constraint by adding a regular penalty term to the model likelihood function; the other is to use model selection.
Using simulated and real data, our proposed methods effectively identified additive interactions and proved to be applicable to real-world data. Simulations were used to evaluate the performance of the methods in scenarios with and without additive interactions. The penalty term converged to 0 with increasing λ, and the final models matched the expected interaction status, demonstrating that regularized estimation could effectively identify additive interactions. Model selection was compared with classical methods (delta and bootstrap) across various scenarios with different interaction strengths, and the additive interactions were closely observed and the results aligned closely with bootstrap results. The coefficients in the model without interaction adhered to a simplifying equation, reinforcing that there was no significant interaction between smoking and alcohol use on oral cancer risk.
In summary, the model selection method based on the Hannan-Quinn criterion (HQ) appears to be a competitive alternative to the bootstrap method for identifying additive interactions. Furthermore, when using RERI, AP, and S to assess the additive interaction, the results are more consistent and the results are simple and easy to understand.
在流行病学中，相对超额交互作用风险（RERI）、归因比例（AP）和协同作用指数（S）等指标常用于评估两个变量之间的相加交互作用。然而，在实际应用中，这些指标的结果有时并不一致，因此可能难以从中得出结论。
基于 RERI、AP 和 S 之间的关系，我们提出了一种结果一致的方法，该方法通过约束 ，并且结果的解释简单明了。我们提出了两种实现这一目标的途径：一种是通过在模型似然函数中添加正则惩罚项来完成约束；另一种是使用模型选择。
使用模拟和真实数据，我们提出的方法有效地识别了相加交互作用，并证明适用于真实数据。模拟用于评估在有和没有相加交互作用的情况下方法的性能。随着 λ 的增加，惩罚项收敛到 0，最终模型与预期的交互状态匹配，表明正则化估计可以有效地识别相加交互作用。模型选择与经典方法（delta 和 bootstrap）进行了比较，涵盖了不同交互强度的各种场景，并且密切观察了相加交互作用，结果与 bootstrap 结果非常吻合。没有交互作用的模型中的系数符合简化方程，这进一步证实了吸烟和饮酒与口腔癌风险之间没有显著的交互作用。
总之，基于 Hannan-Quinn 准则（HQ）的模型选择方法似乎是识别相加交互作用的一种有竞争力的替代 bootstrap 方法。此外，在使用 RERI、AP 和 S 来评估相加交互作用时，结果更加一致，并且结果简单易懂。
