Identification of an additive interaction using parameter regularization and model selection in epidemiology

• Published in: PeerJ (San Francisco, CA) Vol. 12; p. e18304
• Main Authors: Hu, Chanchan, Lin, Zhifeng, Hu, Zhijian, Lin, Shaowei
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 14.10.2024; PeerJ Inc
• Subjects: Additive interactions; Analysis; Computer Simulation; Epidemiological Models; Epidemiology; Health aspects; Humans; Model selection; Models, Statistical; Parameter regularization; Real data; Simulation methods; Smoking - epidemiology; Statistics; Technology application; Additive interactions; Model selection; Parameter regularization; Epidemiology; Real data
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.18304

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.