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E‐synthesis for carcinogenicity assessments: A case study of processed meat
Author(s) -
De Pretis Francesco,
Jukola Saana,
Landes Jürgen
Publication year - 2022
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/jep.13697
Subject(s) - causality (physics) , bayesian network , representation (politics) , bayesian probability , computer science , agency (philosophy) , consumption (sociology) , risk analysis (engineering) , econometrics , medicine , machine learning , artificial intelligence , mathematics , sociology , social science , physics , quantum mechanics , politics , political science , law
Rationale, Aims and Objectives Recent controversies about dietary advice concerning meat demonstrate that aggregating the available evidence to assess a putative causal link between food and cancer is a challenging enterprise. Methods We show how a tool developed for assessing putative causal links between drugs and adverse drug reactions, E‐Synthesis, can be applied for food carcinogenicity assessments. The application is demonstrated on the putative causal relationship between processed meat consumption and cancer. Results The output of the assessment is a Bayesian probability that processed meat consumption causes cancer. This Bayesian probability is calculated from a Bayesian network model, which incorporates a representation of Bradford Hill's Guidelines as probabilistic indicators of causality. We show how to determine probabilities of indicators of causality for food carcinogenicity assessments based on assessments of the International Agency for Research on Cancer. Conclusions We find that E‐Synthesis is a tool well‐suited for food carcinogenicity assessments, as it enables a graphical representation of lines and weights of evidence, offers the possibility to make a great number of judgements explicit and transparent, outputs a probability of causality suitable for decision making and is flexible to aggregate different kinds of evidence.