pp 1–12 | Cite as

Real and Virtual Clinical Trials: A Formal Analysis

  • Barbara OsimaniEmail author
  • Marta Bertolaso
  • Roland Poellinger
  • Emanuele Frontoni


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.


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



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.


  1. Anjum RL, Mumford S (2012) Causal dispositionalism. In: Bird A, Ellis B, Sankey H (eds) Properties, powers and structure, chap. 7. Routledge, New York, pp 101–118Google Scholar
  2. Beisbart C, Norton JD (2012) Why monte carlo simulations are inferences and not experiments. Int Stud Philos Sci 26(4):403–422CrossRefGoogle Scholar
  3. Bertolaso M (2013) On the structure of biological explanations: beyond functional ascriptions in cancer research. Epistemologia 36(1):112–130CrossRefGoogle Scholar
  4. Bertolaso M, Ratti E (2018) Conceptual challenges in the theoretical foundations of systems biology. In: Bizzarri M (ed) Systems biology. Springer, Humana Press, New York, pp 1–13Google Scholar
  5. Bertolaso M, Campaner R (2018) Scientific practice in modelling diseases: stances from cancer research and neuropsychiatry. J Med Philos (forthcoming)Google Scholar
  6. Bertolaso M, Macleod M (eds) (2016) In silico modeling: the human factor. Humana Mente 30:III–XVGoogle Scholar
  7. Boem F, Ratti E (2017) Toward a notion of intervention in Big-data biology and molecular medicine. In: Boniolo G, Nathan MJ (eds) Philosophy of molecular medicine: foundational issues in research and practice. Routledge, New YorkGoogle Scholar
  8. Britton OJ, Bueno-Orovio A, Van Ammel K, Lu HR, Towart R, Gallacher DJ, Rodriguez B (2013) Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology. Proc Natl Acad Sci 110(23):E2098–E2105CrossRefGoogle Scholar
  9. Cartwright N (2007) Are RCTs the Gold Standard? Biosocieties 2:11–20. CrossRefGoogle Scholar
  10. Cartwright N, Stegenga J (2011) A theory of evidence for evidence-based policy, chapter 11. In: Dawid P, Twining W, Vasilaki M (eds) Evidence, inference and enquiry. Oxford University Press, New York, pp 291–322Google Scholar
  11. Carusi A (2014) Validation and variability: dual challenges on the path from systems biology to systems medicine. Stud Hist Philos Sci C 48:28–37Google Scholar
  12. Carusi A, Burrage K, Rodriguez B (2012) Bridging experiments, models and simulations: an integrative approach to validation in computational cardiac electrophysiology. Am J Physiol Heart Circ Physiol 303(2):H144–H155CrossRefGoogle Scholar
  13. Clarke B, Gillies D, Illari P, Russo F, Williamson J (2014) Mechanisms and the evidence hierarchy. Topoi 33(2):339–360CrossRefGoogle Scholar
  14. Corrias A, Giles W, Rodriguez B (2011) Ionic mechanisms of electrophysiological properties and repolarization abnormalities in rabbit Purkinje fibers. Am J Physiol Heart Circ Physiol 300(5):H1806–H1813CrossRefGoogle Scholar
  15. Davies MR, Mistry HB, Hussein L, Pollard CE, Valentin JP, Swinton J, Abi-Gerges N (2012) An in silico canine cardiac midmyocardial action potential duration model as a tool for early drug safety assessment. Am J Physiol Heart Circ Physiol 302(7):H1466–H1480CrossRefGoogle Scholar
  16. Dawid P, Twinning W, Vasilaki M (eds) Evidence, inference and enquiry, chap. 11. Oxford University Press, New York, pp 291–322Google Scholar
  17. Diez Roux AV (2015) The virtual epidemiologist—promise and peril. Am J Epidemiol 181(2):100–102CrossRefGoogle Scholar
  18. Dowe P (1992) Wesley salmon’s process theory of causality and the conserved quantity theory. Philos Sci 59(2):195–216CrossRefGoogle Scholar
  19. Dowe P (2000) Physical causation. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  20. Frigg R, Reiss J (2009) The philosophy of simulation: hot new issues or same old stew? Synthese 169(3):593–613CrossRefGoogle Scholar
  21. Holland PW (1986) Statistics and causal inference. J Am Statist Assoc 81(396):945–960. CrossRefGoogle Scholar
  22. Hoover KD (2008) “Phillips curve.” The concise encyclopedia of economics. Library of economics and liberty. Accessed 29 Aug 2017
  23. Keller EF (2003) Making sense of life: explaining biological development with models, metaphors, and machines. Harvard University Press, CambridgeGoogle Scholar
  24. Landes J, Osimani B, Poellinger R (2017) Epistemology of causal inference in pharmacology. Towards a framework for the assessment of harms. Eur J Philos Sci. Google Scholar
  25. Lewis D (1973a) Counterfactuals. Blackwell Publishers, Oxford (Reprinted with revisions, 1986)Google Scholar
  26. Lewis D (1973b) Causation. J Philos 70(17):556–567CrossRefGoogle Scholar
  27. Lewis D (2000) Causation as influence. J Philos 97(4):182–197CrossRefGoogle Scholar
  28. Mackie JL (1980) The cement of the universe: a study of causation. Oxford University Press, New YorkCrossRefGoogle Scholar
  29. MacLeod M, Nersessian NJ (2013) Coupling simulation and experiment: the bimodal strategy in integrative systems biology. Stud Hist Philos Biol Biomed A 44(4):572–584. CrossRefGoogle Scholar
  30. Morrison M (2015) Reconstructing reality: models, mathematics, and simulations. Oxford University Press, New YorkCrossRefGoogle Scholar
  31. Mumford S (2009) Causal powers and capacities, chap. 12. In: Beebee H, Hitchcock C, Menzies P (eds) The Oxford handbook of causation. Oxford University Press, New York, pp 265–278Google Scholar
  32. Osimani B (2014) Hunting side effects and explaining them: should we reverse evidence hierarchies upside down? Topoi 33(2):295–312. CrossRefGoogle Scholar
  33. Osimani B, Poellinger R (forthcoming) A protocol for model validation and causal inference form computer simulation. Stud Hist Philos Sci CGoogle Scholar
  34. Parke EC (2014) Experiments, simulations, and epistemic privilege. Philos Sci 81(4):516–536CrossRefGoogle Scholar
  35. Parker WS (2009) Does matter really matter? Computer simulations, experiments, and materiality. Synthese 169(3):483–496CrossRefGoogle Scholar
  36. Pearl J (2000) Causality: models, reasoning, and inference, 1st edn. Cambridge University Press, CambridgeGoogle Scholar
  37. Poellinger R (forthcoming) On the ramifications of theory choice in causal assessment: indicators of causation and their conceptual relationships. Philos SciGoogle Scholar
  38. Poellinger R (forthcoming) Analogy-based inference patterns in pharmacological research. In: La Caze A, Osimani B (eds) Uncertainty in pharmacology: epistemology, methods, and decisions. Boston studies in philosophy of science. Springer, New YorkGoogle Scholar
  39. Reichenbach H (1956) The direction of time. University of California Press, Berkeley-Los AngelesGoogle Scholar
  40. Romero L, Pueyo E, Fink M, Rodríguez B (2009) Impact of ionic current variability on human ventricular cellular electrophysiology. Am J Physiol Heart Circ Physiol 297(4): H1436–H1445CrossRefGoogle Scholar
  41. Rowbottom DP (2009) Models in biology and physics: What’s the difference? Found Sci 14(4):281–294CrossRefGoogle Scholar
  42. Rubin D (2005) Causal inference using potential outcomes. J Amer Statist Assoc 100(469):322–331. CrossRefGoogle Scholar
  43. Rubin DB (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 66(5):688–701CrossRefGoogle Scholar
  44. Salmon W (1984) Scientific explanation and the causal structure of the world. Princeton University Press, PrincetonGoogle Scholar
  45. Salmon W (1997) Causality and explanation: a reply to two critiques. Philos Sci 64:461–477CrossRefGoogle Scholar
  46. Sarkar AX, Sobie EA (2011) Quantification of repolarization reserve to understand interpatient variability in the response to proarrhythmic drugs: a computational analysis. Heart Rhythm 8(11):1749–1755CrossRefGoogle Scholar
  47. Spirtes P, Glymour C, Scheines R (2000) Causation, prediction, and search, adaptive computation and machine learning. MIT Press, BostonGoogle Scholar
  48. Sprenger J (2016) The probabilistic no miracles argument. Eur J Philos Sci 6:173–189CrossRefGoogle Scholar
  49. Viceconti M, Henney A, Morley-Fletcher E (2016) In silico clinical trials: how computer simulation will transform the biomedical industry. Int J Clin Trials 3(2):37–46CrossRefGoogle Scholar
  50. Wang R-S, Maron BA, Loscalzo J (2015) Systems medicine: evolution of systems biology from bench to bedside. Wiley Interdisc Rev 7(4):141–161Google Scholar
  51. Winsberg E (2009) A tale of two methods. Synthese 169(3):575–592CrossRefGoogle Scholar
  52. Winslow RL, Helm P, Baumgartner W, Peddi S, Ratnanather T, McVeigh E, Miller MI (2002) Imaging-based integrative models of the heart: closing the loop between experiment and simulation. In ‘In silico simulation of biological processes: Novartis foundation symposium 247, pp 129–143Google Scholar
  53. Worrall J (2007) Evidence in medicine and evidence-based medicine. Philos Compass 2(6):981–1022. CrossRefGoogle Scholar

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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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