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Erschienen in: European Journal of Epidemiology 5/2018

11.04.2018 | CORRESPONDENCE

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

verfasst von: Sander Greenland, Nicholas Patrick Jewell, Mohammad Ali Mansournia

Erschienen in: European Journal of Epidemiology | Ausgabe 5/2018

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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, although matching serves to illustrate our points in a familiar epidemiologic context. …
Literatur
2.
Zurück zum Zitat Mansournia MA, Jewell NP, Greenland S. Case–control matching: effects, misconceptions, and recommendations. Eur J Epidemiol. 2018;33:5–14.CrossRefPubMed Mansournia MA, Jewell NP, Greenland S. Case–control matching: effects, misconceptions, and recommendations. Eur J Epidemiol. 2018;33:5–14.CrossRefPubMed
3.
Zurück zum Zitat Greenland S. For and against methodology: some perspectives on recent causal and statistical inference debates. Eur J Epidemiol. 2017;32(1):3–20.CrossRefPubMed Greenland S. For and against methodology: some perspectives on recent causal and statistical inference debates. Eur J Epidemiol. 2017;32(1):3–20.CrossRefPubMed
4.
Zurück zum Zitat Greenland S. Confounding of incidence density ratio in case–control studies. Epidemiology. 2013;24:624–5.CrossRefPubMed Greenland S. Confounding of incidence density ratio in case–control studies. Epidemiology. 2013;24:624–5.CrossRefPubMed
5.
Zurück zum Zitat Pang M, Schuster T. Confounding of incidence density ratio in case–control studies. Epidemiology. 2013;24:625–7.CrossRefPubMed Pang M, Schuster T. Confounding of incidence density ratio in case–control studies. Epidemiology. 2013;24:625–7.CrossRefPubMed
6.
Zurück zum Zitat 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
7.
Zurück zum Zitat Breslow NE, Day NE. Statistical methods in cancer research. Vol I: the analysis of case–control data. Lyon: IARC; 1980. Breslow NE, Day NE. Statistical methods in cancer research. Vol I: the analysis of case–control data. Lyon: IARC; 1980.
8.
Zurück zum Zitat Rothman KJ. Modern epidemiology. Boston: Little, Brown; 1986. Rothman KJ. Modern epidemiology. Boston: Little, Brown; 1986.
9.
Zurück zum Zitat Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. 3rd ed. Philadelphia: Lippincott Williams and Wilkins; 2008. Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. 3rd ed. Philadelphia: Lippincott Williams and Wilkins; 2008.
10.
Zurück zum Zitat Sheehe PR. Dynamic risk analysis in retrospective matched-pair studies of disease. Biometrics. 1962;18:323–41.CrossRef Sheehe PR. Dynamic risk analysis in retrospective matched-pair studies of disease. Biometrics. 1962;18:323–41.CrossRef
11.
Zurück zum Zitat Greenland S. Cohorts versus dynamic populations: a dissenting view. J Chronic Dis. 1986;39:565–6.CrossRefPubMed Greenland S. Cohorts versus dynamic populations: a dissenting view. J Chronic Dis. 1986;39:565–6.CrossRefPubMed
12.
Zurück zum Zitat Cox DR. The planning of experiments. New York: Wiley; 1958. Cox DR. The planning of experiments. New York: Wiley; 1958.
13.
Zurück zum Zitat Mansournia MA, Etminan M, Danaei G, Kaufman JS, Collins G. Handling time varying confounding in observational research. BMJ. 2017;359:j4587.CrossRefPubMed Mansournia MA, Etminan M, Danaei G, Kaufman JS, Collins G. Handling time varying confounding in observational research. BMJ. 2017;359:j4587.CrossRefPubMed
14.
Zurück zum Zitat Hernán MA, Robins JM. Causal inference. New York: Chapman and Hall; 2018. Hernán MA, Robins JM. Causal inference. New York: Chapman and Hall; 2018.
15.
Zurück zum Zitat Greenland S. Absence of confounding does not correspond to collapsibility of the rate ratio or rate difference. Epidemiology. 1996;7:498–501.CrossRefPubMed Greenland S. Absence of confounding does not correspond to collapsibility of the rate ratio or rate difference. Epidemiology. 1996;7:498–501.CrossRefPubMed
16.
Zurück zum Zitat Hernán MA. The hazards of hazard ratios. Epidemiology. 2009;20:13–5. Hernán MA. The hazards of hazard ratios. Epidemiology. 2009;20:13–5.
19.
Zurück zum Zitat Greenland S, Morgenstern H. Matching and efficiency in cohort studies. Am J Epidemiol. 1990;131:151–9.CrossRefPubMed Greenland S, Morgenstern H. Matching and efficiency in cohort studies. Am J Epidemiol. 1990;131:151–9.CrossRefPubMed
20.
Zurück zum Zitat 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. 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.
21.
Zurück zum Zitat Stürmer T, Brenner H. Degree of matching and gain in power and efficiency in case–control studies. Epidemiology. 2001;12:101–8.CrossRefPubMed Stürmer T, Brenner H. Degree of matching and gain in power and efficiency in case–control studies. Epidemiology. 2001;12:101–8.CrossRefPubMed
22.
Zurück zum Zitat Greenland S. Re: “Estimating relative risk functions in case–control studies using a nonparametric logistic regression”. Am J Epidemiol. 1997;146:883–4.CrossRefPubMed Greenland S. Re: “Estimating relative risk functions in case–control studies using a nonparametric logistic regression”. Am J Epidemiol. 1997;146:883–4.CrossRefPubMed
23.
Zurück zum Zitat Greenland S. Intuitions, simulations, theorems: the role and limits of methodology (invited commentary). Epidemiology. 2012;23:440–2.CrossRefPubMed Greenland S. Intuitions, simulations, theorems: the role and limits of methodology (invited commentary). Epidemiology. 2012;23:440–2.CrossRefPubMed
24.
Zurück zum Zitat Greenland S. Small-sample bias and corrections for conditional maximum-likelihood odds-ratio estimators. Biostatistics. 2000;1:113–22.CrossRefPubMed Greenland S. Small-sample bias and corrections for conditional maximum-likelihood odds-ratio estimators. Biostatistics. 2000;1:113–22.CrossRefPubMed
25.
Zurück zum Zitat Greenland S, Mansournia MA. Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions. Stat Med. 2015;34:3133–43.CrossRefPubMed Greenland S, Mansournia MA. Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions. Stat Med. 2015;34:3133–43.CrossRefPubMed
26.
Zurück zum Zitat Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;352:i1981.CrossRefPubMed Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;352:i1981.CrossRefPubMed
27.
Zurück zum Zitat Sullivan S, Greenland S. Bayesian regression in SAS software. Int J Epidemiol. 2013;42:308–17.CrossRefPubMed Sullivan S, Greenland S. Bayesian regression in SAS software. Int J Epidemiol. 2013;42:308–17.CrossRefPubMed
28.
Zurück zum Zitat Discacciati A, Orsini N, Greenland S. Approximate Bayesian logistic regression via penalized likelihood by data augmentation. Stata J. 2015;15(3):712–36. Discacciati A, Orsini N, Greenland S. Approximate Bayesian logistic regression via penalized likelihood by data augmentation. Stata J. 2015;15(3):712–36.
30.
Zurück zum Zitat 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.CrossRefPubMed 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.CrossRefPubMed
32.
Zurück zum Zitat 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.CrossRefPubMed 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.CrossRefPubMed
33.
Zurück zum Zitat Kalish LA. Reducing mean squared error in the analysis of pair-matched case–control studies. Biometrics. 1990;46:493–9.CrossRefPubMed Kalish LA. Reducing mean squared error in the analysis of pair-matched case–control studies. Biometrics. 1990;46:493–9.CrossRefPubMed
34.
Zurück zum Zitat 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
35.
Zurück zum Zitat Pearl J. Causality: models, reasoning and inference. 2nd ed. Cambridge: Cambridge University Press; 2009.CrossRef Pearl J. Causality: models, reasoning and inference. 2nd ed. Cambridge: Cambridge University Press; 2009.CrossRef
36.
Zurück zum Zitat VanderWeele TJ. Explanation in causal inference: methods for mediation and interaction. New York: Oxford University Press; 2015. VanderWeele TJ. Explanation in causal inference: methods for mediation and interaction. New York: Oxford University Press; 2015.
37.
Zurück zum Zitat Pearl J, Glymour M, Jewell NP. Causal inference in statistics: a primer. New York: Wiley; 2017. Pearl J, Glymour M, Jewell NP. Causal inference in statistics: a primer. New York: Wiley; 2017.
Metadaten
Titel
Theory and methodology: essential tools that can become dangerous belief systems
verfasst von
Sander Greenland
Nicholas Patrick Jewell
Mohammad Ali Mansournia
Publikationsdatum
11.04.2018
Verlag
Springer Netherlands
Erschienen in
European Journal of Epidemiology / Ausgabe 5/2018
Print ISSN: 0393-2990
Elektronische ISSN: 1573-7284
DOI
https://doi.org/10.1007/s10654-018-0395-7

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