European Journal of Epidemiology

, Volume 33, Issue 5, pp 503–506 | Cite as

Theory and methodology: essential tools that can become dangerous belief systems

  • Sander Greenland
  • Nicholas Patrick Jewell
  • Mohammad Ali MansourniaEmail author

We thank Dr. Karp for his interest [1] in our paper [2]. We agree on some points, but our theoretical description differs from his in ways leading to important divergences for teaching and practice. We also see a danger of overextending abstract theory (with its inevitable and extensive simplifications) into practice [3], especially when the practical questions are causal but the theory applied lacks an explicit, sound longitudinal causal model to address these questions. As we will explain, a defect in the “study base” theory Dr. Karp adopts as a foundational belief system is that it takes as a foundation a parameter affected by baseline risk factors—including exposure when that has effects on follow-up or disease. It consequently leads to biases and misconceptions of the sort documented elsewhere [4, 5] and below, which require a coherent theory of longitudinal causality to address. Our divergence from Dr. Karp thus raises the issue of the role of theory and methods in research,...


Case–control studies Causal inference Confounding Epidemiological research 


  1. 1.
    Karp I. Toward eradicating misconceptions on matching in etiological studies. Eur J Epidemiol. 2018. Scholar
  2. 2.
    Mansournia MA, Jewell NP, Greenland S. Case–control matching: effects, misconceptions, and recommendations. Eur J Epidemiol. 2018;33:5–14.CrossRefPubMedGoogle Scholar
  3. 3.
    Greenland S. For and against methodology: some perspectives on recent causal and statistical inference debates. Eur J Epidemiol. 2017;32(1):3–20.CrossRefPubMedGoogle Scholar
  4. 4.
    Greenland S. Confounding of incidence density ratio in case–control studies. Epidemiology. 2013;24:624–5.CrossRefPubMedGoogle Scholar
  5. 5.
    Pang M, Schuster T. Confounding of incidence density ratio in case–control studies. Epidemiology. 2013;24:625–7.CrossRefPubMedGoogle Scholar
  6. 6.
    Allen AS, Glen A, Satten GA. Control for confounding in case–control studies using the stratification score, a retrospective balancing score. Am J Epidemiol. 2011;173:752–60.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Breslow NE, Day NE. Statistical methods in cancer research. Vol I: the analysis of case–control data. Lyon: IARC; 1980.Google Scholar
  8. 8.
    Rothman KJ. Modern epidemiology. Boston: Little, Brown; 1986.Google Scholar
  9. 9.
    Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. 3rd ed. Philadelphia: Lippincott Williams and Wilkins; 2008.Google Scholar
  10. 10.
    Sheehe PR. Dynamic risk analysis in retrospective matched-pair studies of disease. Biometrics. 1962;18:323–41.CrossRefGoogle Scholar
  11. 11.
    Greenland S. Cohorts versus dynamic populations: a dissenting view. J Chronic Dis. 1986;39:565–6.CrossRefPubMedGoogle Scholar
  12. 12.
    Cox DR. The planning of experiments. New York: Wiley; 1958.Google Scholar
  13. 13.
    Mansournia MA, Etminan M, Danaei G, Kaufman JS, Collins G. Handling time varying confounding in observational research. BMJ. 2017;359:j4587.CrossRefPubMedGoogle Scholar
  14. 14.
    Hernán MA, Robins JM. Causal inference. New York: Chapman and Hall; 2018.Google Scholar
  15. 15.
    Greenland S. Absence of confounding does not correspond to collapsibility of the rate ratio or rate difference. Epidemiology. 1996;7:498–501.CrossRefPubMedGoogle Scholar
  16. 16.
    Hernán MA. The hazards of hazard ratios. Epidemiology. 2009;20:13–5.Google Scholar
  17. 17.
    Mansournia MA, Hernán MA, Greenland S. Matched designs and causal diagrams. Int J Epidemiol. 2013;42:860–9.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Cole SR, Hudgens MG, Brookhart MA, Westreich D. Risk. Am J Epidemiol. 2015;181:246–50.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Greenland S, Morgenstern H. Matching and efficiency in cohort studies. Am J Epidemiol. 1990;131:151–9.CrossRefPubMedGoogle Scholar
  20. 20.
    Greenland S. Partial and marginal matching in case–control studies. In: Moolgavkar SH, Prentice RL, editors. Modern statistical methods in chronic disease epidemiology. New York: Wiley; 1986. p. 35–49.Google Scholar
  21. 21.
    Stürmer T, Brenner H. Degree of matching and gain in power and efficiency in case–control studies. Epidemiology. 2001;12:101–8.CrossRefPubMedGoogle Scholar
  22. 22.
    Greenland S. Re: “Estimating relative risk functions in case–control studies using a nonparametric logistic regression”. Am J Epidemiol. 1997;146:883–4.CrossRefPubMedGoogle Scholar
  23. 23.
    Greenland S. Intuitions, simulations, theorems: the role and limits of methodology (invited commentary). Epidemiology. 2012;23:440–2.CrossRefPubMedGoogle Scholar
  24. 24.
    Greenland S. Small-sample bias and corrections for conditional maximum-likelihood odds-ratio estimators. Biostatistics. 2000;1:113–22.CrossRefPubMedGoogle Scholar
  25. 25.
    Greenland S, Mansournia MA. Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions. Stat Med. 2015;34:3133–43.CrossRefPubMedGoogle Scholar
  26. 26.
    Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;352:i1981.CrossRefPubMedGoogle Scholar
  27. 27.
    Sullivan S, Greenland S. Bayesian regression in SAS software. Int J Epidemiol. 2013;42:308–17.CrossRefPubMedGoogle Scholar
  28. 28.
    Discacciati A, Orsini N, Greenland S. Approximate Bayesian logistic regression via penalized likelihood by data augmentation. Stata J. 2015;15(3):712–36.Google Scholar
  29. 29.
    Mansournia MA, Geroldinger A, Greenland S, Heinze G. Separation in logistic regression—causes, consequences, and control. Am J Epidemiol. 2018;187:864–70. Scholar
  30. 30.
    Stürmer T, Brenner H. Flexible matching strategies to increase power and efficiency to detect and estimate gene-environment interactions in case–control studies. Am J Epidemiol. 2002;155:593–602.CrossRefPubMedGoogle Scholar
  31. 31.
    Langholz B, Clayton D. Sampling strategies in nested case–control studies. Environ Health Perspect. 1994;102(Suppl 8):47–51.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Mansson R, Joffe MM, Sun W, Hennessy S. On the estimation and use of propensity scores in case–control and case–cohort studies. Am J Epidemiol. 2007;166:332–9.CrossRefPubMedGoogle Scholar
  33. 33.
    Kalish LA. Reducing mean squared error in the analysis of pair-matched case–control studies. Biometrics. 1990;46:493–9.CrossRefPubMedGoogle Scholar
  34. 34.
    Vandenbroucke JP, Broadbent A, Pearce N. Causality and causal inference in epidemiology: the need for a pluralistic approach. Int J Epidemiol. 2016;45:1776–86.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Pearl J. Causality: models, reasoning and inference. 2nd ed. Cambridge: Cambridge University Press; 2009.CrossRefGoogle Scholar
  36. 36.
    VanderWeele TJ. Explanation in causal inference: methods for mediation and interaction. New York: Oxford University Press; 2015.Google Scholar
  37. 37.
    Pearl J, Glymour M, Jewell NP. Causal inference in statistics: a primer. New York: Wiley; 2017.Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Sander Greenland
    • 1
    • 2
  • Nicholas Patrick Jewell
    • 3
    • 4
  • Mohammad Ali Mansournia
    • 5
    Email author
  1. 1.Department of Epidemiology, Fielding School of Public HealthUniversity of CaliforniaLos AngelesUSA
  2. 2.Department of Statistics, College of Letters and ScienceUniversity of CaliforniaLos AngelesUSA
  3. 3.Division of Biostatistics, School of Public HealthUniversity of CaliforniaBerkeleyUSA
  4. 4.Department of StatisticsUniversity of CaliforniaBerkeleyUSA
  5. 5.Department of Epidemiology and Biostatistics, School of Public HealthTehran University of Medical SciencesTehranIran

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