Biomedical Statistics and Informatics

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Causality in Medicine and Its Relationship with the Role of Statistics

Received: 17 January 2017    Accepted: 04 February 2017    Published: 24 February 2017
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Abstract

The general goal of this work is the clarification of the use of concepts of causality in medicine and its relationship with the role of statistics. The value of an association is the evidence of causality. The Bradford Hill considerations on causality are the criteria commonly used to infer causality. Statistics help to know the role of chance in the working medical hypotheses but does not prevent other common mistakes made during clinical research, such as biases. Man has found a procedure that removes the most of all subjectivities and external factors: the scientific method, this does not mean that scientific studies are infallible. There are many factors influencing the cure or improvement of a disease that would be take in account: spontaneous resolution, regression to the mean, the Forer effect, placebo effect and other. The subjective observation of these phenomena is often insufficient when it comes to analyzing the effectiveness of therapies, medications, diets, homeopathy, cosmetics and natural therapies. It is very difficult to establish causality in health sciences but not impossible, the principles of this establishement can be resumed as Temporality, Strength, Consistency, Biology, Plausibility, Specificity, Analogy, Experiment and Coherence.

DOI 10.11648/j.bsi.20170202.14
Published in Biomedical Statistics and Informatics (Volume 2, Issue 2, June 2017)
Page(s) 61-68
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Statistics, Causality, Medicine, Mathematics, Epidemiology

References
[1] Glass TA, Goodman SN, Hernán MA, Samet JM; Causal inference in public health; Annual Review of Public Health; 2013; 34; 61–75.
[2] Hill, Austin Bradford; The Environment and Disease: Association or Causation?; Proceedings of the Royal Society of Medicine; 1965; 58 (5); 295–300.
[3] Höfler M; The Bradford Hill considerations on causality: a counterfactual perspective?; Emerging themes in epidemiology; 2005. 2 (1); 11.
[4] Monleón-Getino, Toni; El tratamiento numérico de la realidad. Reflexiones sobre la importancia actual de la estadística en la Sociedad de la Información; Arbor; 2010; 186; 743.
[5] De Regil LM, Casanova EP; Racionalidad científica, causalidad y metaanálisis de ensayos clínicos; Salud Pública de México; 2008; 50 (6); 523-28.
[6] Banegas JR, Rodriguez Artalejo F. Inferencia causal en Epidemiologia. En: Método Epidemiologico. Manual Docente de la ENS.. Royo Bordonada MA, Damián Moreno J. ICSIII, M. E. C. Madrid 2009. N. I. P. O.: 477-09-019-9.
[7] Katz, David L; Clinical Epidemiology & Evidence-Based Medicine: Fundamental Principles of Clinical Reasoning & Research; London (UK); SAGE; 2001.
[8] Broadbent A, Vandenbroucke JP, Pearce. Formalism or pluralism? A reply to commentaries on 'Causality and causal inference in epidemiology'. N. Int J Epidemiol. 2017 Jan 27.
[9] Bertram DA, Flynn K, Alligood E; Endovascular Placed Grafts for Infrarenal Abdominal Aortic Aneurisms: A Systematic Review of Published Studies of Effectiveness. Technology Assessment Program, Report n. 9; Boston, Health Services Research & Development Service, Veteran Affairs Medical Center; 1998.
[10] Doi, S. A. R; Understanding evidence in health care: Using clinical epidemiology; South Yarra, VIC, Australia: Palgrave Macmillan; 2012.
[11] Grobbee, D. E.; Hoes, Arno W; Clinical Epidemiology: Principles, Methods, and Applications for Clinical Research; Jones & Bartlett Learning; 2009.
[12] Shang A, Huwiler-Muntener K, Nartey L, Juni P, Dorig S, Sterne JA, Pewsner D, Egger M; Are the clinical effects of homoeopathy placebo effects? Comparative study of placebo-controlled trials of homoeopathy and allopathy; Lancet; 2005; 366 (9487); 726-732.
[13] Howick J, Glasziou P, Aronson JK; The evolution of evidence hierarchies: what can Bradford Hill's 'guidelines for causation' contribute?; Journal of the Royal Society of Medicine; 2009; 102 (5); 186–94.
[14] Rothman KJ, Greenland S. Causation and causal inference. En: Rothman KJ, Greenland S. Modern Epidemiology. 2ª ed. Philadelphia: Lippincott-Raven, 1998.
[15] Howick, Jeremy H; The Philosophy of Evidence-based Medicine; USA; Wiley; 2011.
[16] Rao CR; Statistics and Truth; Teaching of Psychology; 1989; 12; 229-230.
[17] Williams D. Arouet, François-Marie [Voltaire] (1694–1778). Oxford Dictionary of National Biography. 2004.
[18] Monleón-Getino T; Importancia de Darwin en el desarrollo de la estadística moderna; Estadística Española; 2010; 175; 371-392.
[19] Galton, F. Regression Towards Mediocrity in Hereditary Stature. Journal of the Anthropological Institute; 1886; 15; 246–263.
[20] Ross SM; Introducción a la estadística; Editorial Reverte; Madrid (Spain); 2007.
[21] Karylowski J; Regression Toward the Mean Effect: No Statistical Background Required; Teaching of Psychology; 1985; 12; 229-230.
[22] Smith Gary; Do Statistics Test Scores Regress Toward the Mean? Chance; 1977; 10(4).
[23] James KE; Regression toward the Mean in Uncontrolled Clinical Studies. Biometrics; 1973; 29 (1); 121-130.
[24] Forer BR; The Fallacy of Personal Validation: A classroom Demonstration of Gullibility; Journal of Abnormal Psychology; 1949; 44; 118-121.
[25] Dickson DH, Kelly IW; The 'Barnum Effect' in Personality Assessment: A Review of the Literature; Psychological Reports; 1985; 57; 367-382.
[26] Marks D & Kammann R; The Psychology of the psychic. Buffalo (NY, USA); Prometheus; 2000.
Author Information
  • Section of Statistics, Departament of Genetics, Microbiology and Statistics, Faculty of Biology, Univeristy of Barcelona, Barcelona, Spain; Group of Researh in Bioestatistics and Bioinformatics (GRBIO), Barcelona, Spain

  • Department of Public Health, School of Medicine, Univeristy of Barcelona, Barcelona, Spain

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  • APA Style

    Antonio Monleon-Getino, Jaume Canela-Soler. (2017). Causality in Medicine and Its Relationship with the Role of Statistics. Biomedical Statistics and Informatics, 2(2), 61-68. https://doi.org/10.11648/j.bsi.20170202.14

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    ACS Style

    Antonio Monleon-Getino; Jaume Canela-Soler. Causality in Medicine and Its Relationship with the Role of Statistics. Biomed. Stat. Inform. 2017, 2(2), 61-68. doi: 10.11648/j.bsi.20170202.14

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    AMA Style

    Antonio Monleon-Getino, Jaume Canela-Soler. Causality in Medicine and Its Relationship with the Role of Statistics. Biomed Stat Inform. 2017;2(2):61-68. doi: 10.11648/j.bsi.20170202.14

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  • @article{10.11648/j.bsi.20170202.14,
      author = {Antonio Monleon-Getino and Jaume Canela-Soler},
      title = {Causality in Medicine and Its Relationship with the Role of Statistics},
      journal = {Biomedical Statistics and Informatics},
      volume = {2},
      number = {2},
      pages = {61-68},
      doi = {10.11648/j.bsi.20170202.14},
      url = {https://doi.org/10.11648/j.bsi.20170202.14},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.bsi.20170202.14},
      abstract = {The general goal of this work is the clarification of the use of concepts of causality in medicine and its relationship with the role of statistics. The value of an association is the evidence of causality. The Bradford Hill considerations on causality are the criteria commonly used to infer causality. Statistics help to know the role of chance in the working medical hypotheses but does not prevent other common mistakes made during clinical research, such as biases. Man has found a procedure that removes the most of all subjectivities and external factors: the scientific method, this does not mean that scientific studies are infallible. There are many factors influencing the cure or improvement of a disease that would be take in account: spontaneous resolution, regression to the mean, the Forer effect, placebo effect and other. The subjective observation of these phenomena is often insufficient when it comes to analyzing the effectiveness of therapies, medications, diets, homeopathy, cosmetics and natural therapies. It is very difficult to establish causality in health sciences but not impossible, the principles of this establishement can be resumed as Temporality, Strength, Consistency, Biology, Plausibility, Specificity, Analogy, Experiment and Coherence.},
     year = {2017}
    }
    

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    AB  - The general goal of this work is the clarification of the use of concepts of causality in medicine and its relationship with the role of statistics. The value of an association is the evidence of causality. The Bradford Hill considerations on causality are the criteria commonly used to infer causality. Statistics help to know the role of chance in the working medical hypotheses but does not prevent other common mistakes made during clinical research, such as biases. Man has found a procedure that removes the most of all subjectivities and external factors: the scientific method, this does not mean that scientific studies are infallible. There are many factors influencing the cure or improvement of a disease that would be take in account: spontaneous resolution, regression to the mean, the Forer effect, placebo effect and other. The subjective observation of these phenomena is often insufficient when it comes to analyzing the effectiveness of therapies, medications, diets, homeopathy, cosmetics and natural therapies. It is very difficult to establish causality in health sciences but not impossible, the principles of this establishement can be resumed as Temporality, Strength, Consistency, Biology, Plausibility, Specificity, Analogy, Experiment and Coherence.
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