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Topoi

pp 1–12 | Cite as

Real and Virtual Clinical Trials: A Formal Analysis

  • Barbara Osimani
  • Marta Bertolaso
  • Roland Poellinger
  • Emanuele Frontoni
Article

Abstract

If well-designed, the results of a Randomised Clinical Trial (RCT) can justify a causal claim between treatment and effect in the study population; however, additional information might be needed to carry over this result to another population. RCTs have been criticized exactly on grounds of failing to provide this sort of information (Cartwright and Stegenga, in: Dawid, Twining, Vasilaki (eds) Evidence, inference and enquiry. Oxford University Press, New York, 2011), as well as to black-box important details regarding the mechanisms underpinning the causal law instantiated by the RCT result. On the other side, so-called In Silico Clinical Trials (ISCTs) face the same criticisms addressed against standard modelling and simulation techniques, and cannot be equated to experiments (see, e.g.; Boem and Ratti in: Boniolo, Nathan (eds) Philosophy of molecular medicine: foundational issues in research and practice, Routledge, New York, 2017; Parker in Synthese 169(3):483–496, 2009; Parke in Philos Sci 81(4):516–536, 2014; Diez Roux in Am J Epidemiol 181(2):100–102, 2015 and related discussions in Frigg and Reiss in Synthese 169(3):593–613, 2009; Winsberg in Synthese 169(3):575–592, 2009; Beisbart and Norton in Int Stud Philos Sci 26(4):403–422, 2012). We undertake a formal analysis of both methods in order to identify their distinct contribution to causal inference in the clinical setting. Britton et al.’s study (Proc Natl Acad Sci 110(23):E2098–E2105, 2013) on the impact of ion current variability on cardiac electrophysiology is used for illustrative purposes. We deduce that, by predicting variability through interpolation, ISCTs aid with problems regarding extrapolation of RCTs results, and therefore in assessing their external validity. Furthermore, ISCTs can be said to encode “thick” causal knowledge (knowledge about the biological mechanisms underpinning the causal effects at the clinical level)—as opposed to “thin” difference-making information inferred from RCTs. Hence, ISCTs and RCTs cannot replace one another but rather, they are complementary in that the former provide information about the determinants of variability of causal effects, while the latter can, under certain conditions, establish causality in the first place.

Keywords

Randomised Clinical Trials In Silico Clinical Trials Computational modeling and simulation External validity Extrapolation Interpolation 

Notes

Funding

This study was funded by the European Research Council (Grant Number: GA 639276).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Logic and Philosophy of Science, Department of Biomedical Sciences and Public Health, Faculty of MedicineUniversità Politecnica delle MarcheAnconaItaly
  2. 2.Philosophy of Science, Faculty of Engineering, Institute of Philosophy of Scientific and Technological PracticeUniversità Campus Bio-Medico di RomaRomaItaly
  3. 3.Munich Center for Mathematical Philosophy, Fakultät für Philosophie, Wissenschaftstheorie und ReligionswissenschaftLudwig-Maximilians-Universität MünchenMunichGermany
  4. 4.Foundations of Informatics and Computer Vision, Department of Information Engineering, Faculty of EngineeringUniversità Politecnica delle MarcheAnconaItaly

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