Pharmacovigilance as Personalized Evidence

Pretis, Francesco De; Peden, William; Landes, Jürgen; Osimani, Barbara

doi:10.1007/978-3-030-74804-3_8

Cited by 3 publications

(3 citation statements)

References 48 publications

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“…We now show how E‐Synthesis 20–25 can be employed as a tool for food carcinogenicity assessment, see also the Appendix (A Primer on Bayesian Reasoning).…”

Section: Methodsmentioning

confidence: 99%

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E‐synthesis for carcinogenicity assessments: A case study of processed meat

Pretis

Jukola

Landes

2022

Evaluation Clinical Practice

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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.

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“…We now show how E‐Synthesis 20–25 can be employed as a tool for food carcinogenicity assessment, see also the Appendix (A Primer on Bayesian Reasoning).…”

Section: Methodsmentioning

confidence: 99%

“…E‐Synthesis is an emerging tool for drug safety assessments, 20–25 modelling confounding and biases of observational studies and RCTs. It offers a principled approach to produce a posterior probability of causal relationships given evidence produced by diverse observational and randomized studies.…”

Section: Introductionmentioning

confidence: 99%

E‐synthesis for carcinogenicity assessments: A case study of processed meat

Pretis

Jukola

Landes

2022

Evaluation Clinical Practice

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“…Prediction of outcomes in individuals based on Personalized Medicine relies mainly on two tenets: knowledge about the causal models underpinning the data, and accurate assignment of the individual to relevant reference classes [39]. Indeed, the problem of identifying proper subgroups explaining variance in the data is of a different nature than that of assigning a given individual to any of such subgroups [39]. Such challenges are still open, but they do not diminish the importance of individualizing treatments in Medicine making the most of the knowledge and inferential tools available…”

Section: Methodological and Ethical Challengesmentioning

confidence: 99%

Mass versus personalized medicine againstCOVID‐19 in the “system sciences” era

et al. 2022

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Current philosophical perspectives on drug approval in the real world

Landes,

Auker-Howlett

2024

Journal of Causal Inference

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The evidence-based medicine approach to causal medical inference is the dominant account among medical methodologists. Competing approaches originating in the philosophy of medicine seek to challenge this account. In order to see how successful these challenges are, we need to assess the performance of all approaches in real world medical inference. One important real world problem all approaches could be applied to is the assessment of drugs for approval by drug regulation agencies. This study assesses the success of the status quo against an empirical non-systematically obtained body of evidence and we scrutinise the alternative approaches from the armchair, contemplating how they would fare in the real world. We tentatively conclude that the status quo is regularly not successful at its primary task as it regularly fails to correctly assess effectiveness and safety and suggest that this is due to inherent factors of the “messy real world.” However, while all alternatives hold promise, they are at least as susceptible to the real world issues that beset the status quo. We also make recommendations for changes to current drug approval procedures, identify lacunae to fill in the alternatives, and finally, call for a continuation of the development of alternative approaches to causal medical inference and recommendations for changes to current drug approval procedures.

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Pharmacovigilance as Personalized Evidence

Cited by 3 publications

References 48 publications

E‐synthesis for carcinogenicity assessments: A case study of processed meat

E‐synthesis for carcinogenicity assessments: A case study of processed meat

Mass versus personalized medicine againstCOVID‐19 in the “system sciences” era

Current philosophical perspectives on drug approval in the real world

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