Abstract
Mario Bunge’s medical philosophy emphasizes the importance of mechanismic models in guiding the design, analysis, and practical application of clinical research. By contrast, the Evidence-Based Medicine (EBM) movement regards mechanismic hypotheses as “evidence” dissociable from, and of secondary importance to, the findings of experimental research. In agreement with Bunge, it is argued here that mechanismic models and mechanismic thinking play essential roles in both clinical research and practice. Mechanismic models in medicine view health and disease as emergent processes occurring in complex biological systems and draw upon established scientific knowledge from multiple disciplines to help identify and control parameters that have decisive effects on clinical outcomes. Models play an essential role in designing efficient and reliable population-based studies, and in detecting and correcting for random error and systematic bias in clinical research. They are important both for extrapolating the results of clinical research to novel contexts and for tailoring interventions to the specific circumstances of an individual case. Contrary to the subordinate status they are accorded by EBM, empirically-validated mechanismic models should constitute the foundation of a scientific approach to medicine.
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References
Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An Empiricist’s companion. Princeton: Princeton University Press.
Aström, K. J., & Murray, R. M. (2008). Feedback systems: An introduction for scientists and engineers. Princeton: Princeton University Press.
Bareinboim, E., & Pearl, J. (2016). Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences USA, 113(27), 7345–7352.
Bunge, M. (2004). How does it work?: The search for explanatory mechanisms. Philosophy of the Social Sciences, 34(2), 182–210.
Bunge, M. (2013). Medical philosophy: Conceptual issues in medicine. Hackensack: World Scientific Publishing Company.
Bunge, M. (2017). Philosophy of science. Volume 2: From explanation to justification. New York: Routledge.
Bunge, M., & Mahner, M. (2001). Scientific realism: Selected essays of Mario Bunge. Amherst: Prometheus Books.
Cartwright, N., & Deaton, A. (2017). Understanding and misunderstanding randomized controlled trials. Social Science and Medicine. https://doi.org/10.1016/j.socscimed.2017.12.005.
Davey Smith, G., & Hemani, G. (2014). Mendelian randomization: Genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics, 23(R1), R89–R98. https://doi.org/10.1093/hmg/ddu328.
Dwan, K., Altman, D. G., Arnaiz, J. A., Bloom, J., Chan, A. W., Cronin, E., Decullier, E., Easterbrook, P. J., Von Elm, E., Gamble, C., Ghersi, D., Ioannidis, J. P., Simes, J., & Williamson, P. R. (2008). Systematic review of the empirical evidence of study publication bias and outcome reporting bias. Public Library of Science One, 3(8), e3081. https://doi.org/10.1371/journal.pone.0003081.
Eddy, D. M. (2005). Evidence-based medicine: A unified approach. Health Affairs (Millwood), 24(1), 9–17.
Greenhalgh, T., Howick, J., Maskrey, N., & Evidence-Based Medicine Renaissance Group. (2014). Evidence based medicine: A movement in crisis. British Medical Journal, 348, g3725. https://doi.org/10.1136/bmj.g3725.
Howick, J., Glasziou, P., & Aronson, J. K. (2010). Evidence-based mechanistic reasoning. Journal of the Royal Society of Medicine, 103(11), 433–441.
Imai, K., King, G., & Stuart, E. (2008). Misunderstandings among experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society, Series A, 171(Part 2), 481–502.
Ioannidis, J. P. (2005). Why most published research findings are false. Public Library of Science Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124.
Ioannidis, J. P., Stuart, M. E., Brownlee, S., & Strite, S. A. (2017). How to survive the medical misinformation mess. European Journal of Clinical Investigation, 47(11), 795–802.
Kent, D. M., & Hayward, R. A. (2007). Limitations of applying summary results of clinical trials to individual patients: The need for risk stratification. Journal of the American Medical Association, 298(10), 1209–1212.
King, G., Nielsen, R., Coberley, C., Pope, J. E., & Wells, A. (2011). Avoiding randomization failure in program evaluation, with application to the Medicare Health Support program. Population Health Management, 14(Suppl 1), S11–S22. https://doi.org/10.1089/pop.2010.0074.
Lenzer, J., Hoffman, J. R., Furberg, C. D., Ioannidis, J. P., & Guideline Panel Review Working Group. (2013). Ensuring the integrity of clinical practice guidelines: A tool for protecting patients. British Medical Journal, 347, f5535. https://doi.org/10.1136/bmj.f5535.
Machta, B. B., Ricky Chachra, R., Mark, K., Transtrum, M. K., & Sethna, J. P. (2013). Parameter space compression underlies emergent theories and predictive models. Science, 342(6158), 604–607.
Murad, M. H., Montori, V. M., Ioannidis, J. P., Jaeschke, R., Devereaux, P. J., Prasad, K., Neumann, I., Carrasco-Labra, A., Agoritsas, T., Hatala, R., Meade, M. O., Wyer, P., Cook, D. J., & Guyatt, G. (2014). How to read a systematic review and meta-analysis and apply the results to patient care: Users’ guides to the medical literature. Journal of the American Medical Association, 312(2), 171–179.
Pearl, J. (2009). Causality: Models, reasoning and inference. Cambridge: Cambridge University Press.
Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. New York: Wiley.
Snowden, T. J., van der Graaf, P. H., & Tindall, M. J. (2017). Methods of model reduction for large-scale biological systems: A survey of current methods and trends. Bulletin of Mathematical Biology, 79(7), 1449–1486.
Transtrum, M. K., & Qiu, P. (2016). Bridging mechanistic and phenomenological models of complex biological systems. Public Library of Science Computational Biology, 12(5), e1004915. https://doi.org/10.1371/journal.pcbi.1004915.
Varadhan, R., Segal, J. B., Boyd, C. M., Wu, A. W., & Weiss, C. O. (2013). A framework for the analysis of heterogeneity of treatment effect in patient-centered outcomes research. Journal of Clinical Epidemiology, 66(8), 818–825.
White, A., Tolman, M., Thames, H. D., Withers, H. R., Mason, K. A., & Transtrum, M. K. (2016). The limitations of model-based experimental design and parameter estimation in sloppy systems. Public Library of Science Computational Biology, 12, e1005227. https://doi.org/10.1371/journal.pcbi.1005227.
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.
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Ahmad, O. (2019). Mechanisms in Clinical Research and Medical Practice. In: Matthews, M.R. (eds) Mario Bunge: A Centenary Festschrift. Springer, Cham. https://doi.org/10.1007/978-3-030-16673-1_39
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DOI: https://doi.org/10.1007/978-3-030-16673-1_39
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