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

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References

  1. Karp I. Toward eradicating misconceptions on matching in etiological studies. Eur J Epidemiol. 2018. https://doi.org/10.1007/s10654-018-0376-x.

    Article  PubMed  Google Scholar 

  2. Mansournia MA, Jewell NP, Greenland S. Case–control matching: effects, misconceptions, and recommendations. Eur J Epidemiol. 2018;33:5–14.

    Article  PubMed  Google Scholar 

  3. Greenland S. For and against methodology: some perspectives on recent causal and statistical inference debates. Eur J Epidemiol. 2017;32(1):3–20.

    Article  PubMed  Google Scholar 

  4. Greenland S. Confounding of incidence density ratio in case–control studies. Epidemiology. 2013;24:624–5.

    Article  PubMed  Google Scholar 

  5. Pang M, Schuster T. Confounding of incidence density ratio in case–control studies. Epidemiology. 2013;24:625–7.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Breslow NE, Day NE. Statistical methods in cancer research. Vol I: the analysis of case–control data. Lyon: IARC; 1980.

    Google Scholar 

  8. Rothman KJ. Modern epidemiology. Boston: Little, Brown; 1986.

    Google Scholar 

  9. Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. 3rd ed. Philadelphia: Lippincott Williams and Wilkins; 2008.

    Google Scholar 

  10. Sheehe PR. Dynamic risk analysis in retrospective matched-pair studies of disease. Biometrics. 1962;18:323–41.

    Article  Google Scholar 

  11. Greenland S. Cohorts versus dynamic populations: a dissenting view. J Chronic Dis. 1986;39:565–6.

    Article  PubMed  CAS  Google Scholar 

  12. Cox DR. The planning of experiments. New York: Wiley; 1958.

    Google Scholar 

  13. Mansournia MA, Etminan M, Danaei G, Kaufman JS, Collins G. Handling time varying confounding in observational research. BMJ. 2017;359:j4587.

    Article  PubMed  Google Scholar 

  14. Hernán MA, Robins JM. Causal inference. New York: Chapman and Hall; 2018.

    Google Scholar 

  15. Greenland S. Absence of confounding does not correspond to collapsibility of the rate ratio or rate difference. Epidemiology. 1996;7:498–501.

    Article  PubMed  CAS  Google Scholar 

  16. Hernán MA. The hazards of hazard ratios. Epidemiology. 2009;20:13–5.

    Google Scholar 

  17. Mansournia MA, Hernán MA, Greenland S. Matched designs and causal diagrams. Int J Epidemiol. 2013;42:860–9.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Cole SR, Hudgens MG, Brookhart MA, Westreich D. Risk. Am J Epidemiol. 2015;181:246–50.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Greenland S, Morgenstern H. Matching and efficiency in cohort studies. Am J Epidemiol. 1990;131:151–9.

    Article  PubMed  CAS  Google Scholar 

  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. Stürmer T, Brenner H. Degree of matching and gain in power and efficiency in case–control studies. Epidemiology. 2001;12:101–8.

    Article  PubMed  Google Scholar 

  22. Greenland S. Re: “Estimating relative risk functions in case–control studies using a nonparametric logistic regression”. Am J Epidemiol. 1997;146:883–4.

    Article  PubMed  CAS  Google Scholar 

  23. Greenland S. Intuitions, simulations, theorems: the role and limits of methodology (invited commentary). Epidemiology. 2012;23:440–2.

    Article  PubMed  Google Scholar 

  24. Greenland S. Small-sample bias and corrections for conditional maximum-likelihood odds-ratio estimators. Biostatistics. 2000;1:113–22.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  26. Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;352:i1981.

    Article  PubMed  Google Scholar 

  27. Sullivan S, Greenland S. Bayesian regression in SAS software. Int J Epidemiol. 2013;42:308–17.

    Article  PubMed  Google Scholar 

  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. Mansournia MA, Geroldinger A, Greenland S, Heinze G. Separation in logistic regression—causes, consequences, and control. Am J Epidemiol. 2018;187:864–70. https://doi.org/10.1093/aje/kwx299.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  31. Langholz B, Clayton D. Sampling strategies in nested case–control studies. Environ Health Perspect. 1994;102(Suppl 8):47–51.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  33. Kalish LA. Reducing mean squared error in the analysis of pair-matched case–control studies. Biometrics. 1990;46:493–9.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Pearl J. Causality: models, reasoning and inference. 2nd ed. Cambridge: Cambridge University Press; 2009.

    Book  Google Scholar 

  36. VanderWeele TJ. Explanation in causal inference: methods for mediation and interaction. New York: Oxford University Press; 2015.

    Google Scholar 

  37. Pearl J, Glymour M, Jewell NP. Causal inference in statistics: a primer. New York: Wiley; 2017.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA

    Sander Greenland

  2. Department of Statistics, College of Letters and Science, University of California, Los Angeles, CA, USA

    Sander Greenland

  3. Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA

    Nicholas Patrick Jewell

  4. Department of Statistics, University of California, Berkeley, CA, USA

    Nicholas Patrick Jewell

  5. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box 14155-6446, Tehran, Iran

    Mohammad Ali Mansournia

Authors
  1. Sander Greenland
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  2. Nicholas Patrick Jewell
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  3. Mohammad Ali Mansournia
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Correspondence to Mohammad Ali Mansournia.

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Greenland, S., Jewell, N.P. & Mansournia, M.A. Theory and methodology: essential tools that can become dangerous belief systems. Eur J Epidemiol 33, 503–506 (2018). https://doi.org/10.1007/s10654-018-0395-7

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  • DOI: https://doi.org/10.1007/s10654-018-0395-7

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