On identification of agonistic interaction: Hepatitis B and C interaction on hepatocellular carcinoma

Sheng-Hsuan Lin, Yen Tsung Huang*, Hwai I. Yang

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Agonistic interaction is one of the most important types of mechanistic interaction, which is difficult to be distinguished from synergistic interaction by empirical data. In this study, we propose four approaches that suffice to identify and estimate the agonistic interaction: (1) to make a strong assumption that synergism does not exist; (2) to exploit information from a third factor by assuming that this factor is a necessary component for the background condition of synergistic interaction but is not involved in other mechanisms; (3) to consider a third factor necessary for the background condition of agonistic interaction but not involved in other mechanisms; and (4) similar to (3) but to allow flexibility that the third factor may have a main effect on the outcome and/or a synergistic effect with the two risk factors of interest. 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; HBV carrier n = 4149 (17.44%), HCV carrier n = 1313 (5.52%)). The result demonstrated 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. By exploiting an additional risk factor that satisfies certain assumptions, these approaches potentially fill the gap between mechanistic and causal interactions, contributing the comprehensive understanding of causal mechanisms.

Original languageEnglish
Pages (from-to)2467-2476
Number of pages10
JournalStatistics in Medicine
Volume38
Issue number13
DOIs
StatePublished - 15 Jun 2019

Keywords

  • agonist
  • causal inference
  • interaction
  • mechanism investigation
  • sufficient component cause model

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