Skip to main content

Introduction: The History of Statistics in Medicine and Surgery

  • Chapter
  • First Online:
Pediatric and Congenital Cardiac Care
  • 1546 Accesses

Abstract

Cardiac surgery has been quantitative from its onset. As the field progressed, surgeons encountered questions that required going beyond existing and traditional methods, fostering both adoption of analytic methods from non-medical fields (communication, industrial sciences, and physics, for example) and development of new ones. These were underpinned by specific philosophies of science about uncertainty, causes of surgical failure as a result of human error on the one hand and lack of scientific progress on the other, and how to express effectiveness and appropriateness to inform the timing of surgery and its indications. Included were traditional methods such as confidence limits and P-values, but also appreciation of why human error takes limited forms, as studied by human factors and cognitive researchers. The “incremental risk factor concept” reinterpreted variables associated with outcomes, initially in the context of congenital heart disease. New methods were either developed within the discipline or introduced, including those for survival analysis and competing risks that accounted for non-proportional hazards by temporal decomposition and separate risk factors for different time frames of follow-up. More recently, longitudinal methods to examine binary, ordinal, and continuous outcomes were developed. Propensity-score–based methods for comparative effectiveness studies, particularly in light of the limited ability to randomize treatments, enabled identifying complementary rather than competing techniques. However, just as the evolution of surgery has not stopped, neither has the quest for better methods to answer surgeons’ questions. Increasingly, these require advanced algorithmic data analytic methods, such as those developing in the field of genomic informatics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kirklin JW, Dushane JW, Patrick RT, Donald DE, Hetzel PS, Harshbarger HG, et al. Intracardiac surgery with the aid of a mechanical pump-oxygenator system (Gibbon type): report of eight cases. Proc Staff Meet Mayo Clin. 1955;30:201–6.

    CAS  PubMed  Google Scholar 

  2. Blackstone EH. Born of necessity: the dynamic synergism between advancement of analytic methods and generation of new knowledge. J Heart Valve Dis. 1995;4:326–36.

    CAS  PubMed  Google Scholar 

  3. David FN. Games, gods & gambling: a history of probability and statistical ideas. London: Charles Griffin; 1962.

    Google Scholar 

  4. Hacking J. The emergence of probability. Cambridge: Cambridge University Press; 1975. p. 102.

    Google Scholar 

  5. Galilei G. Sopra le scoperte dei dadi, as summarized in R. Langley: practical statistics simply expanded. New York: Dover; 1970.

    Google Scholar 

  6. Blackstone EH. Thinking beyond the risk factors. Eur J Cardiothorac Surg. 2006;29:645–52.

    PubMed  Google Scholar 

  7. Ware JH, Mosteller F, Delgado F, Donnelly C, Ingelfinger AJ. P values. In: Bailar JC, Mosteller F, editors. Medical uses of statistics. 2nd ed. Boston: NEJM Books; 1992.

    Google Scholar 

  8. Grunkemeier GL, Wu Y, Furnary AP. What is the value of a p value? Ann Thorac Surg. 2009;87:1337–43.

    PubMed  Google Scholar 

  9. Hubbard R, Lindsay RM. Why P values are not a useful measure of evidence in statistical significance testing. Theor Psychol. 2008;18:69–88.

    Google Scholar 

  10. Senn S. Two cheers for P-values? J Epidemiol Biostat. 2001;6:194–204.

    Google Scholar 

  11. Barnard GA. Must clinical trials be large? The interpretation of P-values and the combination of test results. Stat Med. 1990;9:601–14.

    CAS  PubMed  Google Scholar 

  12. Kempthorne O. Of what use are tests of significance and tests of hypotheses? Commun Statist Theor Method A. 1976;5:763.

    Google Scholar 

  13. Salsburg D. Hypothesis versus significance testing for controlled clinical trials: a dialogue. Stat Med. 1990;9:201–11.

    CAS  PubMed  Google Scholar 

  14. Burack JH, Impellizzeri P, Homel P, Cunningham Jr JN. Public reporting of surgical mortality: a survey of New York State cardiothoracic surgeons. Ann Thorac Surg. 1999;68:1195–200.

    CAS  PubMed  Google Scholar 

  15. Fisher RA. Statistical methods and scientific inference. 3rd ed. New York: Hafner; 1973.

    Google Scholar 

  16. Richardson MH. The gradual elimination of the preventable disaster from surgery. Thorac Med Assoc. 1912:181.

    Google Scholar 

  17. Gawande A. Complications: a surgeon’s notes on an imperfect science. New York: Metropolitan Books; 2002.

    Google Scholar 

  18. Kirklin JW, Karp RB. The tetralogy of Fallot, from a surgical point of view. Philadelphia: WB Saunders; 1970.

    Google Scholar 

  19. Rizzoli G, Blackstone EH, Kirklin JW, Pacifico AD, Bargeron Jr LM. Incremental risk factors in hospital mortality rate after repair of ventricular septal defect. J Thorac Cardiovasc Surg. 1980;80:494–505.

    CAS  PubMed  Google Scholar 

  20. Lawrence AC. Human error as a cause of accidents in gold mining. J Safety Res. 1974;6:78–88.

    Google Scholar 

  21. Wigglesworth EC. A teaching model of injury causation and a guide for selecting countermeasures. Occup Psychol. 1972;46:69–78.

    Google Scholar 

  22. Berry DA, Stangl DK. Meta-analysis in medicine and health policy. New York: Marcel Dekker; 2000.

    Google Scholar 

  23. McIntyre N, Popper K. The critical attitude in medicine: the need for a new ethics. Br Med J (Clin Res Ed). 1983;287:1919.

    CAS  Google Scholar 

  24. Christensen JF, Levinson W, Dunn PM. The heart of darkness: the impact of perceived mistakes on physicians. J Gen Intern Med. 1992;7:424–31.

    CAS  PubMed  Google Scholar 

  25. Wu AW, Folkman S, McPhee SJ, Lo B. Do house officers learn from their mistakes? JAMA. 1991;265:2089–94.

    CAS  PubMed  Google Scholar 

  26. Reason JT, Carthey J, de Leval MR. Diagnosing “vulnerable system syndrome”: an essential prerequisite to effective risk management. Qual Health Care. 2001;10 Suppl 2:ii21–5.

    PubMed Central  PubMed  Google Scholar 

  27. Cook RI, Wreathall J, Smith A, Cronin DC, Rivero O, Harland RC, et al. Probabilistic risk assessment of accidental ABO-incompatible thoracic organ transplantation before and after 2003. Transplantation. 2007;84:1602–9.

    PubMed  Google Scholar 

  28. Cooper JB, Newbower RS, Kitz RJ. An analysis of major errors and equipment failures in anesthesia management: considerations for prevention and detection. Anesthesiology. 1984;60:34–42.

    CAS  PubMed  Google Scholar 

  29. Leape LL. Error in medicine. JAMA. 1994;272:1851–7.

    CAS  PubMed  Google Scholar 

  30. Leape LL, Lawthers AG, Brennan TA, Johnson WG. Preventing medical injury. QRB Qual Rev Bull. 1993;19:144–9.

    CAS  PubMed  Google Scholar 

  31. Reason J. Combating omission errors through task analysis and good reminders. Qual Saf Health Care. 2002;11:40–4.

    CAS  PubMed Central  PubMed  Google Scholar 

  32. Hinske LC, Sandmeyer B, Urban B, Hinske PM, Lackner CK, Lazarovici M. The human factor in medical emergency simulation. AMIA Annu Symp Proc. 2009;2009:249–53.

    PubMed Central  PubMed  Google Scholar 

  33. Carthey J, de Leval MR, Reason JT. The human factor in cardiac surgery: errors and near misses in a high technology medical domain. Ann Thorac Surg. 2001;72:300–5.

    CAS  PubMed  Google Scholar 

  34. de Leval MR, Carthey J, Wright DJ, Farewell VT, Reason JT. Human factors and cardiac surgery: a multicenter study. J Thorac Cardiovasc Surg. 2000;119:661–72.

    PubMed  Google Scholar 

  35. Lew RA, Day Jr CL, Harrist TJ, Wood WC, Mihm Jr MC. Multivariate analysis. Some guidelines for physicians. JAMA. 1983;249:641–3.

    CAS  PubMed  Google Scholar 

  36. Gordon T. Statistics in a prospective study: the Framingham Study. In: Gail MH, Johnson NL, editors. Proceedings of the American Statistical Association: sesquicentennial invited paper sessions. Alexandria: ASA; 1989. p. 719–26.

    Google Scholar 

  37. Walker SH, Duncan DB. Estimation of the probability of an event as a function of several independent variables. Biometrika. 1967;54:167–79.

    CAS  PubMed  Google Scholar 

  38. Kannel WB, McGee D, Gordon T. A general cardiovascular risk profile: the Framingham Study. Am J Cardiol. 1976;38:46–51.

    CAS  PubMed  Google Scholar 

  39. Kirklin JW. A letter to Helen (presidential address). J Thorac Cardiovasc Surg. 1979;78:643–54.

    CAS  PubMed  Google Scholar 

  40. Verhulst PF. Notice sur la loi que la population suit dans son accroissement. Math Phys. 1838;10:113.

    Google Scholar 

  41. Verhulst PF. Recherches mathematiques sur la loi d’accroissement de la population. Nouv Mem Acad R Sci Belleslett Brux. 1845;18:1.

    Google Scholar 

  42. Pearl R, Reed LT. On the rate of growth of the population of the United States since 1790 and its mathematical representation. Proc Natl Acad Sci. 1920;6:275–88.

    CAS  PubMed Central  PubMed  Google Scholar 

  43. Cornfield J, Gordon T, Smith WS. Quantal response curves for experimentally uncontrolled variables. Bull Int Stat Inst. 1961;3:97–115.

    Google Scholar 

  44. Berkson J, Flexner LB. On the rate of reaction between enzyme and substrate. J Gen Phys. 1928;11:433–57.

    CAS  Google Scholar 

  45. Kouchoukos NT, Blackstone EH, Hanley FL, Kirklin J. Cardiac surgery. 4th ed. Philadelphia: Elsevier; 2012. p. 251–352.

    Google Scholar 

  46. Gordon T, Kannel WB. Predisposition to atherosclerosis in the head, heart, and legs. The Framingham study. JAMA. 1972;221:661–6.

    CAS  PubMed  Google Scholar 

  47. Bland JM. Sample size in guidelines trials. Fam Pract. 2000;17:S17–20.

    PubMed  Google Scholar 

  48. Berkson J. Application of the logistic function to bioassay. J Am Stat Assoc. 1944;39:357–65.

    CAS  Google Scholar 

  49. Gordon T, Kannel WB. Multiple risk functions for predicting coronary heart disease: the concept, accuracy, and application. Am Heart J. 1982;103:1031–9.

    CAS  PubMed  Google Scholar 

  50. Formigari L. Chain of being. In: Wiener PP, editor. Dictionary of the history of ideas: studies of selected pivotal ideas. New York: Charles Scribner’s Sons; 1968. p. 325–35.

    Google Scholar 

  51. Reason JT. Human error. Cambridge: Cambridge University Press; 1990.

    Google Scholar 

  52. Hrushesky WJ. Triumph of the trivial. Perspect Biol Med. 1998;41:341–8.

    Google Scholar 

  53. Kirklin JK, Blackstone EH. Notes from the editors: figures. J Thorac Cardiovasc Surg. 1994;107:1175–7.

    Google Scholar 

  54. Murphy RD, Papps PC. Construction of mortality tables from the records of insured lives. New York: The Actuarial Society of America; 1922.

    Google Scholar 

  55. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958;53:457–81.

    Google Scholar 

  56. Cox DR. Regression models and life tables. J Roy Statist Soc Ser B. 1972;34:187–220.

    Google Scholar 

  57. Blackstone EH, Kirklin JW, Pluth JR, Turner ME, Parr GV. The performance of the Braunwald-Cutter aortic prosthetic valve. Ann Thorac Surg. 1977;23:302–18.

    CAS  PubMed  Google Scholar 

  58. Nelson W. Theory and applications of hazard plotting for censored failure data. Technometrics. 1972;14:945–66.

    Google Scholar 

  59. Nelson W. Graphical analysis of system repair data. J Qual Technol. 1988;20:24–35.

    Google Scholar 

  60. Nelson W. Confidence limits for recurrence data: applied to cost or number of product repairs. Technometrics. 1995;37:147–57.

    Google Scholar 

  61. Halley E. An estimate of the degrees of the mortality of mankind, drawn from curious tables of the births and funerals of the city of Breslau. Philos Trans R Soc Lond. 1693;17:596.

    Google Scholar 

  62. Gompertz B. On the nature of the function expressive of the law of human mortality. Philos Trans R Soc Lond. 1825;115:513–83.

    Google Scholar 

  63. Gehan EA. A generalized Wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika. 1965;52:203–25.

    CAS  PubMed  Google Scholar 

  64. Lilienfeld DE, Pyne DA. On indices of mortality: deficiencies, validity, and alternatives. J Chronic Dis. 1979;32:463–8.

    CAS  PubMed  Google Scholar 

  65. Mantel N. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep. 1966;50:163–70.

    CAS  PubMed  Google Scholar 

  66. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22:719–48.

    CAS  PubMed  Google Scholar 

  67. O’Neill TJ. Distribution-free estimation of cure time. Biometrika. 1979;66:184–7.

    Google Scholar 

  68. Peto R, Peto J. Asymptotically efficient rank invariant test procedures. J R Statist Soc A. 1972;135:185–207.

    Google Scholar 

  69. Prentice RL, Marek P. A qualitative discrepancy between censored data rank tests. Biometrics. 1979;35:861–7.

    CAS  PubMed  Google Scholar 

  70. Wilcoxon F. Individual comparisons by ranking methods. Biomet Bull. 1947;1:80–3.

    Google Scholar 

  71. Graunt J. Natural and political observations made upon the bills of mortality. Baltimore: Johns Hopkins University Press; 1939. 1662. Reprint.

    Google Scholar 

  72. Grunkemeier GL, Thomas DR, Starr A. Statistical considerations in the analysis and reporting of time-related events. Application to analysis of prosthetic valve-related thromboembolism and pacemaker failure. Am J Cardiol. 1977;39:257–8.

    CAS  PubMed  Google Scholar 

  73. Blackstone EH, Naftel DC, Turner Jr ME. The decomposition of time-varying hazard into phases, each incorporating a separate stream of concomitant information. J Am Stat Assoc. 1986;81:615–24.

    Google Scholar 

  74. Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data. 2nd ed. New York: Wiley; 2002.

    Google Scholar 

  75. David HA, Moeschberger ML. The theory of competing risks. New York: Macmillan; 1978.

    Google Scholar 

  76. Prentice RL, Kalbfleisch JD, Peterson Jr AV, Flournoy N, Farewell VT, Breslow NE. The analysis of failure times in the presence of competing risks. Biometrics. 1978;34:541–54.

    CAS  PubMed  Google Scholar 

  77. Andersen PK, Hansen LS, Keiding N. Assessing the influence of reversible disease indicators on survival. Stat Med. 1991;10:1061–7.

    CAS  PubMed  Google Scholar 

  78. Berkson J, Hollander F. Chemistry–on the equation for the reaction between invertase and sucrose. J Wash Acad Sci. 1930;20:157.

    CAS  Google Scholar 

  79. Smedira NG, Hoercher KJ, Lima B, Mountis MM, Starling RC, Thuita L, et al. Unplanned hospital readmissions after HeartMate II implantation. J Am Coll Cardiol Heart Fail. 2013;1:31–9.

    Google Scholar 

  80. Blackstone EH. Actuarial and Kaplan-Meier survival analysis: there is a difference. J Thorac Cardiovasc Surg. 1999;118:973–5.

    PubMed  Google Scholar 

  81. Fontan F, Kirklin JW, Fernandez G, Costa F, Naftel DC, Tritto F, et al. Outcome after a “perfect” Fontan operation. Circulation. 1990;81:1520–36.

    CAS  PubMed  Google Scholar 

  82. Hickey MS, Blackstone EH, Kirklin JW, Dean LS. Outcome probabilities and life history after surgical mitral commissurotomy: implications for balloon commissurotomy. J Am Coll Cardiol. 1991;17:29–42.

    CAS  PubMed  Google Scholar 

  83. Diggle PJ, Heagerty PJ, Liang KY, Zeger SL. Analysis of longitudinal data. 2nd ed. New York: Oxford University Press; 2002.

    Google Scholar 

  84. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38:963–74.

    CAS  PubMed  Google Scholar 

  85. Morris CN. Parametric empirical Bayes inference: theory and applications. J Am Stat Assoc. 1983;78:47–55.

    Google Scholar 

  86. Goldstein H. Multilevel statistical models. 2nd ed. London: Arnold; 1995.

    Google Scholar 

  87. Steyerberg EW, Eijkemans MJ, Harrell Jr FE, Habbema JD. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med. 2000;19:1059–79.

    CAS  PubMed  Google Scholar 

  88. Chalmers I. Comparing like with like: some historical milestones in the evolution of methods to create unbiased comparison groups in therapeutic experiments. Int J Epidemiol. 2001;30:1156–64.

    CAS  PubMed  Google Scholar 

  89. Dodgson SJ. The evolution of clinical trials. J Eur Med Writers Assoc. 2006;15:20–1.

    Google Scholar 

  90. Milne I, Chalmers I. A controlled clinical trial in 1809? J Epidemiol Community Health. 2002;56:1.

    CAS  PubMed Central  PubMed  Google Scholar 

  91. Bhudia SK, McCarthy PM, Kumpati GS, Helou J, Hoercher KJ, Rajeswaran J, et al. Improved outcomes after aortic valve surgery for chronic aortic regurgitation with severe left ventricular dysfunction. J Am Coll Cardiol. 2007;49:1465–71.

    PubMed  Google Scholar 

  92. Bunker JP, Barnes BA, Mosteller F, editors. Costs, risks, and benefits of surgery. New York: Oxford University Press; 1977.

    Google Scholar 

  93. Burton PR, Gurrin LC, Campbell MJ. Clinical significance not statistical significance: a simple Bayesian alternative to p values. J Epidemiol Community Health. 1998;52:318–23.

    CAS  PubMed Central  PubMed  Google Scholar 

  94. Weinstein MC. Allocation of subjects in medical experiments. N Engl J Med. 1974;291:1278–85.

    CAS  PubMed  Google Scholar 

  95. Burdette WI, Gehan EA. Planning and analysis of clinical studies. Springfield: Charles C Thomas; 1970.

    Google Scholar 

  96. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55.

    Google Scholar 

  97. Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med. 1997;127:757–63.

    CAS  PubMed  Google Scholar 

  98. Blackstone EH. Comparing apples and oranges. J Thorac Cardiovasc Surg. 2002;123:8–15.

    PubMed  Google Scholar 

  99. Rubin DB. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat Med. 2007;26:20–36.

    PubMed  Google Scholar 

  100. Newton I. Philosophiae Naturalis Principia Mathematica; 1687.

    Google Scholar 

  101. Stigler SM. Statistics on the table: the history of statistical concepts and methods. Cambridge: Harvard University Press; 2002.

    Google Scholar 

  102. Gillham NW. Sir Francis Galton and the birth of eugenics. Annu Rev Genet. 2001;35:82–101.

    Google Scholar 

  103. Fisher RA. Statistical methods for research workers. 1925. Reprint, 14th edition. New York: Hafner; 1970.

    Google Scholar 

  104. Howie D. Interpreting probability: controversies and developments in the early twentieth century. Cambridge: Cambridge University Press; 2002.

    Google Scholar 

  105. de Leval MR, Dubini G, Migliavacca F, Jalali H, Camporini G, Redington A, et al. Use of computational fluid dynamics in the design of surgical procedures: application to the study of competitive flows in cavo-pulmonary connections. J Thorac Cardiovasc Surg. 1996;111:502–13.

    PubMed  Google Scholar 

  106. Cooley JW, Tukey JW. An algorithm for machine calculation of complex Fourier series. Math Comput. 1965;19:297–301.

    Google Scholar 

  107. Breiman L. Bagging predictors. Mach Learn. 1996;24:123–40.

    Google Scholar 

  108. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. New York: Springer; 2001.

    Google Scholar 

  109. Zhang M, Zhang D, Wells MT. Variable selection for large p small n regression models with incomplete data: mapping QTL with epistases. BMC Bioinforma. 2008;9:251–9.

    Google Scholar 

  110. Giesl J. Current trends in automated deduction. Künstl Intell. 2010;24:11–3.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eugene H. Blackstone MD .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag London

About this chapter

Cite this chapter

Blackstone, E.H. (2015). Introduction: The History of Statistics in Medicine and Surgery. In: Barach, P., Jacobs, J., Lipshultz, S., Laussen, P. (eds) Pediatric and Congenital Cardiac Care. Springer, London. https://doi.org/10.1007/978-1-4471-6587-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-6587-3_2

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6586-6

  • Online ISBN: 978-1-4471-6587-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics