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Real and Virtual Clinical Trials: a Formal Analysis

Osimani, Barbara and Marta, Bertolaso and Roland, Poellinger and Emanuele, Frontoni (2019) Real and Virtual Clinical Trials: a Formal Analysis. [Preprint]

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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 & Stegenga 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 & Ratti, 2017, Parker, 2009, Parke, 2014, Diez Roux, 2015 and related discussions in Frigg & Reiss, 2009, Winsberg, 2009, and Beisbart & Norton, 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 (Britton et al., 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.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Osimani, Barbarabarbaraosimani@gmail.com0000-0001-5212-9525
Marta, Bertolasom.bertolaso@unicampus.it0000-0003-2241-7029
Roland, Poellinger0000-0002-0487-4034
Emanuele, Frontonie.frontoni@univpm.it0000-0002-8893-9244
Keywords: Randomised Clinical Trials, In Silico Clinical Trials, Computational modeling and simulation, External validity, Extrapolation, Interpolation.
Subjects: Specific Sciences > Biology
General Issues > Causation
Specific Sciences > Medicine > Clinical Trials
General Issues > Computer Simulation
General Issues > Confirmation/Induction
General Issues > Evidence
General Issues > Experimentation
General Issues > Formal Learning Theory
General Issues > History of Science Case Studies
Specific Sciences > Medicine
General Issues > Models and Idealization
Depositing User: Prof. Barbara Osimani
Date Deposited: 27 Jan 2021 15:14
Last Modified: 27 Jan 2021 15:14
Item ID: 18640
Official URL: https://link.springer.com/article/10.1007/s11245-0...
DOI or Unique Handle: https://doi.org/10.1007/s11245-018-9563-3
Subjects: Specific Sciences > Biology
General Issues > Causation
Specific Sciences > Medicine > Clinical Trials
General Issues > Computer Simulation
General Issues > Confirmation/Induction
General Issues > Evidence
General Issues > Experimentation
General Issues > Formal Learning Theory
General Issues > History of Science Case Studies
Specific Sciences > Medicine
General Issues > Models and Idealization
Date: 2019
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/18640

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