The importance of data: Monitoring variables in causal inference with medical imaging

Autores de IIS La Fe

• Carlos Manuel Baracaldo Silva

  Autor

• Pedro Miguel Martínez Gironés

  Autor

• Adrián Galiana Bordera

  Autor

• Leonor Cerdá Alberich

  Autor

• 

  Luis Martí Bonmatí

  Autor

Participantes ajenos a IIS La Fe

• Martí-Aguado, D
• Villar, CG

Grupos

• Investigacion Biomédica en Imagen (GIBI 2-30)

Abstract

Background and objective: In scientific studies with medical imaging, it is important that the process involved in selecting the explanatory variables is closely monitored, as this can lead to significant confounding biases in its methodology. The aim of this paper is to explore whether it is feasible to use criteria for causation (such as those outlined by Bradford Hill) when selecting variables in articles published in the journal RADIOLOGIA. Material and methods: We selected the July-August issue of RADIOLOG & Iacute;A, issue 4 of 2023. Four original articles with sample sizes of at least 100 patients were chosen and their follow-up of causation was analysed. Results: The criteria of temporality, consistency, coherence, plausibility and analogy were observed. The criteria of specificity, experiment, strength and biological gradient were not consistently applied. The methodological robustness and quality of these studies was also noted. Conclusion: Bradford Hill's criteria for causation are useful for the selection of variables in medical imaging studies. The most relevant criteria in medical imaging are temporality, consistency, coherence, plausibility and analogy. (c) 2024 SERAM. Published by Elsevier Espana, S.L.U. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Keywords

• Explanatory variables; Causation; Confounding bias; Observational study; Bradford Hill criteria