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A0246
Title: Visualization and identification of agonistic interaction hepatitis B and C interaction on hepatocellular carcinoma Authors:  Sheng-Hsuan Lin - Institute of Statistics (Taiwan) [presenting]
Yen-Tsung Huang - Academia Sinica (Taiwan)
Wen-Chung Lee - National Taiwan University (Taiwan)
Hwai-I Yang - Academia Sinica (Taiwan)
Abstract: Sufficient-component cause (SCC) framework, as one of the most polished techniques for the methodology development of causal inference, has the advantage of visualizing the interaction effect by synergism or antagonism. However, it is well known that statistical interaction occurs even there is no synergism and antagonism, and vice versa. We propose a modified version of SCC, termed exclusive sufficient causal (eSCC) model, and incorporate this model to both counterfactual and DAGs framework. The causal effects can be interpreted as the additive probabilities of conditions under eSCC. When two exposures of interest are considered, eSCC can visualize the existence of agonism, one important subtype of interaction other than synergism and antagonism. We further propose four approaches that suffice to identify and estimate the agonistic interaction by empirical data. We applied the proposed methods to quantify the agonism of Hepatitis B and C viruses (HBV and HCV) infections on liver cancer using a Taiwanese cohort study ($n=23,820$). The result demonstrates that agonistic interaction is more dominant compared with synergistic interaction, which explains the findings that the dual infected patients do not have a significantly higher risk of liver cancer than those with single infection. This method fills the gap between causal interaction and mechanistic interaction and contributes to a comprehensive understanding of mechanistic investigation.