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Caution: work in progress

While the methodological “revolution” deserves in-depth study, clinical researchers and senior epidemiologists should not be disenfranchised

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References

  1. Sudan M, Arah OA, Olsen J, Kheifets L. Reported associations between asthma and acute lymphoblastic leukemia: insights from a hybrid simulation study. Eur J Epidemiol. 2016. doi:10.1007/s10654-016-0126-x.

    Google Scholar 

  2. Dhana K, van Rosmalen J, Vistisen D, Ikram MA, Hofman A, Franco OH, Kavousi M. Trajectories of body mass index before the diagnosis of cardiovascular disease: a latent class trajectory analysis. Eur J Epidemiol. 2016. doi: 10.1007/s10654-016-0131-0.

    Google Scholar 

  3. Luque-Fernandez MA, Zoega H, Valdimarsdottir U, Williams MA. Deconstructing the smoking-preeclampsia paradox through a counterfactual framework. Eur J Epidemiol. 2016. doi:10.1007/s10654-016-0139-5.

    PubMed  Google Scholar 

  4. Li R, Daniel R, Rachet B. How much do tumor stage and treatment explain socioeconomic inequalities in breast cancer survival? Applying causal mediation analysis to population-based data. Eur J Epidemiol. 2016. doi:10.1007/s10654-016-0155-5.

    Google Scholar 

  5. Porta M, Vineis P, Bolúmar F. The current deconstruction of paradoxes: one sign of the ongoing methodological “revolution”. Eur J Epidemiol. 2015;30:1079–87.

    Article  PubMed  Google Scholar 

  6. Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016. doi:10.1016/j.jclinepi.2016.04.014.

    PubMed  Google Scholar 

  7. Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;27(352):i1981.

    Article  Google Scholar 

  8. Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183:758–64.

    Article  PubMed  Google Scholar 

  9. Pearce N. Analysis of matched case–control studies. BMJ. 2016;352:i969.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Greenland S. Concepts and pitfalls in measuring and interpreting attributable fractions, prevented fractions, and causation probabilities. Ann Epidemiol. 2015;25:155–61.

    Article  PubMed  Google Scholar 

  11. Goodman SN, Fanelli D, Ioannidis JP. What does research reproducibility mean? Sci Transl Med. 2016;8:341ps12.

    Article  PubMed  Google Scholar 

  12. Ioannidis JP, Fanelli D, Dunne DD, Goodman SN. Meta-research: evaluation and improvement of research methods and practices. PLoS Biol. 2015;13:e1002264.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Greenland S, Pearce N. Statistical foundations for model-based adjustments. Annu Rev Public Health. 2015;36:89–108.

    Article  PubMed  Google Scholar 

  14. Soonawala D, Dekkers OM, Vandenbroucke JP, Egger M. Noninferiority is (too) common in noninferiority trials. J Clin Epidemiol. 2016;71:118–20.

    Article  PubMed  Google Scholar 

  15. 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 

  16. Vandenbroucke JP, Broadbent A, Pearce N. Causality and causal inference in epidemiology: the need for a pluralistic approach. Int J Epidemiol. 2016. doi:10.1093/ije/dyv341.

    PubMed  Google Scholar 

  17. Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S. Good practices for quantitative bias analysis. Int J Epidemiol. 2014;43:1969–85.

    Article  PubMed  Google Scholar 

  18. Greenland S, Daniel R, Pearce N. Outcome modelling strategies in epidemiology: traditional methods and basic alternatives. Int J Epidemiol. 2016;45:565–75.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Pizzi C, Pearce N, Richiardi L. Noncollapsibility in studies based on nonrepresentative samples. Ann Epidemiol. 2015;25:955–8.

    Article  PubMed  Google Scholar 

  20. Bolúmar F, Porta M. Epidemiologic methods: beyond clinical medicine, beyond epidemiology. Eur J Epidemiol. 2004;19:733–5.

    Article  PubMed  Google Scholar 

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

    Book  Google Scholar 

  22. Hernán MA, Robins JM. Causal inference. New York: Chapman & Hall/CRC; 2017. http://www.hsph.harvard.edu/miguel-hernan/causal-inference-book.

  23. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37–48.

    Article  CAS  PubMed  Google Scholar 

  24. Robins JM. Data, design, and background knowledge in etiologic inference. Epidemiology. 2001;12:313–20.

    Article  CAS  PubMed  Google Scholar 

  25. Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol. 2002;155:176–84.

    Article  PubMed  Google Scholar 

  26. Hernández-Díaz S, Schisterman EF, Hernán MA. The birth weight “paradox” uncovered? Am J Epidemiol. 2006;164:1115–20.

    Article  PubMed  Google Scholar 

  27. Hernández-Díaz S, Wilcox AJ, Schisterman EF, Hernán MA. From causal diagrams to birth weight-specific curves of infant mortality. Eur J Epidemiol. 2008;23:163–6.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Snoep JD, Morabia A, Hernández-Díaz S, Hernán MA, Vandenbroucke JP. A structural approach to Berkson’s fallacy. And a guide to a history of opinions about it. Int J Epidemiol. 2014;43:515–21.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Greenland S. Quantifying biases in causal models: classical confounding vs. collider-stratification bias. Epidemiology. 2003;14:300–6.

    PubMed  Google Scholar 

  30. Richiardi L, Barone-Adesi F, Merletti F, Pearce N. Using directed acyclic graphs to consider adjustment for socioeconomic status in occupational cancer studies. J Epidemiol Community Health. 2008;62:e14.

    Article  CAS  PubMed  Google Scholar 

  31. Pearce N, Richiardi L. Three worlds collide: Berkson’s bias, selection bias and collider bias. Int J Epidemiol. 2014;43:521–4.

    Article  PubMed  Google Scholar 

  32. Aalen O, Røysland K, Gran J, Kouyos R, Lange T. Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms. Stat Methods Med Res. 2014;1:1-21. http://smm.sagepub.com/content/early/2014/03/27/0962280213520436.full.pdf+html. Accessed 7 July 2016.

  33. Geneletti SG, Gallo V, Porta M, Khoury MJ, Vineis P. Assessing causal relationships in genomics: from Bradford–Hill criteria to complex gene–environment interactions and directed acyclic graphs. Emerg Themes Epidemiol. 2011;8:5. http://www.ete-online.com/content/8/1/5.

  34. Pearl J. Are economists smarter than epidemiologists? Comments on Imbens’s recent paper. Causal analysis in theory and practice [Blog]. October 27, 2014. http://www.mii.ucla.edu/causality/?p=1241. Accessed 7 July 2016.

  35. Greenland S, Mansournia MA. Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness. Eur J Epidemiol. 2015. doi:10.1007/s10654-015-9995-7.

    PubMed  Google Scholar 

  36. Glass TA, Goodman SN, Hernán MA, Samet JM. Causal inference in public health. Annu Rev Public Health. 2013;34:61–75.

    Article  PubMed  PubMed Central  Google Scholar 

  37. VanderWeele TJ, Vansteelandt S, Robins JM. Marginal structural models for sufficient cause interactions. Am J Epidemiol. 2010;1717:506–14.

    Article  Google Scholar 

  38. Robins JM, Hernán MA, Brumbach B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–60.

    Article  CAS  PubMed  Google Scholar 

  39. Van der Weele TJ, Hernán MA. From counterfactuals to sufficient component causes and vice versa. Eur J Epidemiol. 2006;21:855–8.

    Google Scholar 

  40. Hernán MA, Robins JM. Instruments for causal inference:an epidemiologist’s dream? Epidemiology. 2006;17:360–72.

    Article  PubMed  Google Scholar 

  41. Tong S, Neale RE, Shen X, Olsen J. Challenges for epidemiologic research on the verge of a new era. Eur J Epidemiol. 2011;26:689–94.

    Article  PubMed  Google Scholar 

  42. Pearce N. Epidemiology in a changing world: variation, causation and ubiquitous risk factors. Int J Epidemiol. 2011;40:503–12.

    Article  PubMed  Google Scholar 

  43. Porta M, Álvarez-Dardet C. Epidemiology: bridges over (and across) roaring levels. J Epidemiol Community Health. 1998;52:605.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Hernán MA, Clayton D, Keiding N. The Simpson’s paradox unraveled. Int J Epidemiol. 2011;40:780–5.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hernán MA, Alonso A, Logan R, Grodstein F, Michels KB, Willett WC, et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology. 2008;19:766–79.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Dal Maso L, Torelli N, Biancotto E, Di Maso M, Gini A, Franchin G, Levi F, La Vecchia C, Serraino D, Polesel J. Combined effect of tobacco smoking and alcohol drinking in the risk of head and neck cancers: a re-analysis of case–control studies using bi-dimensional spline models. Eur J Epidemiol. 2016;31:385–93.

    Article  CAS  PubMed  Google Scholar 

  47. Teljeur C, Kelly A, Loane M, Densem J, Dolk H. Using scan statistics for congenital anomalies surveillance: the EUROCAT methodology. Eur J Epidemiol. 2015;30:1165–73.

    Article  PubMed  Google Scholar 

  48. Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN, Altman DG. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016;31:337–50.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wang A, Arah OA. G-computation demonstration in causal mediation analysis. Eur J Epidemiol. 2015;30:1119–27.

    Article  PubMed  Google Scholar 

  50. Rydell M, Granath F, Cnattingius S, Magnusson C, Galanti MR. In-utero exposure to maternal smoking is not linked to tobacco use in adulthood after controlling for genetic and family influences: a Swedish sibling study. Eur J Epidemiol. 2014;29:499–506.

    Article  PubMed  Google Scholar 

  51. Sjölander A, Lee W, Källberg H, Pawitan Y. Bounds on sufficient-cause interaction. Eur J Epidemiol. 2014;29:813–20.

    Article  PubMed  Google Scholar 

  52. Niedziela J, Hudzik B, Niedziela N, Gąsior M, Gierlotka M, Wasilewski J, Myrda K, Lekston A, Poloński L, Rozentryt P. The obesity paradox in acute coronary syndrome: a meta-analysis. Eur J Epidemiol. 2014;29:801–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Stenzel SL, Ahn J, Boonstra PS, Gruber SB, Mukherjee B. The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification. Eur J Epidemiol. 2015;30:413–23.

    Article  PubMed  Google Scholar 

  54. Hofman A, Brusselle GG, Darwish Murad S, van Duijn CM, Franco OH, Goedegebure A, Ikram MA, Klaver CC, Nijsten TE, Peeters RP, Stricker BH, Tiemeier HW, Uitterlinden AG, Vernooij MW. The Rotterdam Study: 2016 objectives and design update. Eur J Epidemiol. 2015;30:661–708.

    Article  PubMed  PubMed Central  Google Scholar 

  55. de Groot MC, Klungel OH, Leufkens HG, van Dijk L, Grobbee DE, van de Garde EM. Sources of heterogeneity in case–control studies on associations between statins, ACE-inhibitors, and proton pump inhibitors and risk of pneumonia. Eur J Epidemiol. 2014;29:767–75.

    Article  PubMed  Google Scholar 

  56. Bamia C, Trichopoulos D. An anatomy of the way composite scores work. Eur J Epidemiol. 2015;30:473–83.

    Article  PubMed  Google Scholar 

  57. Thygesen LC, Ersbøll AK. When the entire population is the sample: strengths and limitations in register-based epidemiology. Eur J Epidemiol. 2014;29:551–8.

    Article  PubMed  Google Scholar 

  58. Waldram A, McKerr C, Gobin M, Adak G, Stuart JM, Cleary P. Control selection methods in recent case–control studies conducted as part of infectious disease outbreaks. Eur J Epidemiol. 2015;30:465–71.

    Article  PubMed  Google Scholar 

  59. O’Doherty MG, Jørgensen T, Borglykke A, Brenner H, Schöttker B, Wilsgaard T, et al. Repeated measures of body mass index and C-reactive protein in relation to all-cause mortality and cardiovascular disease: results from the consortium on health and ageing network of cohorts in Europe and the United States (CHANCES). Eur J Epidemiol. 2014;29:887–97.

    Article  PubMed  Google Scholar 

  60. Kim RS. A new comparison of nested case–control and case–cohort designs and methods. Eur J Epidemiol. 2015;30:197–207.

    Article  PubMed  Google Scholar 

  61. de Keyser CE, Leening MJ, Romio SA, Jukema JW, Hofman A, Ikram MA, Franco OH, Stijnen T, Stricker BH. Comparing a marginal structural model with a Cox proportional hazard model to estimate the effect of time-dependent drug use in observational studies: statin use for primary prevention of cardiovascular disease as an example from the Rotterdam Study. Eur J Epidemiol. 2014;29:841–50.

    Article  PubMed  Google Scholar 

  62. Korevaar TI, Steegers EA, de Rijke YB, Schalekamp-Timmermans S, Visser WE, Hofman A, Jaddoe VW, Tiemeier H, Visser TJ, Medici M, Peeters RP. Reference ranges and determinants of total hCG levels during pregnancy: the Generation R Study. Eur J Epidemiol. 2015;30:1057–66.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Medina-Gomez C, Felix JF, Estrada K, Peters MJ, Herrera L, Kruithof CJ, Duijts L, Hofman A, van Duijn CM, Uitterlinden AG, Jaddoe VW, Rivadeneira F. Challenges in conducting genome-wide association studies in highly admixed multi-ethnic populations: the Generation R Study. Eur J Epidemiol. 2015;30:317–30.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Keita AK, Fenollar F, Socolovschi C, Ratmanov P, Bassene H, Sokhna C, Tall A, Mediannikov O, Raoult D. The detection of vector-borne-disease-related DNA in human stool paves the way to large epidemiological studies. Eur J Epidemiol. 2015;30:1021–6.

    Article  PubMed  Google Scholar 

  65. Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30:543–52.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Ioannidis JP. Evidence-based medicine has been hijacked: a report to David Sackett. J Clin Epidemiol. 2016;73:82–6.

    Article  PubMed  Google Scholar 

  67. Ioannidis JP. Why most clinical research is not useful. PLoS Med. 2016;13:e1002049.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Erren TC, Shaw DM, Groß JV. How to avoid haste and waste in occupational, environmental and public health research. J Epidemiol Community Health. 2015;69:823–5.

    Article  CAS  PubMed  Google Scholar 

  69. Morabia A. Has epidemiology become infatuated with methods? A historical perspective on the place of methods during the classical (1945–1965) phase of epidemiology. Annu Rev Public Health. 2015;36:69–88.

    Article  PubMed  Google Scholar 

  70. Porta M, Fernandez E, Belloc J, Malats N, Gallén M, Alonso J. Emergency admission for cancer: a matter of survival? Br J Cancer. 1998;77:477–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Fernandez E, Porta M, Malats N, Belloc J, Gallén M. Symptom to diagnosis interval and survival in cancers of the digestive tract. Dig Dis Sci. 2002;47:2434–40.

    Article  PubMed  Google Scholar 

  72. Macià F, Pumarega J, Gallén M, Porta M. Time from (clinical or certainty) diagnosis to treatment onset in cancer patients: the choice of diagnostic date strongly influences differences in therapeutic delay by tumor site and stage. J Clin Epidemiol. 2013;66:928–39.

    Article  PubMed  Google Scholar 

  73. Porta M, Malats N, Corominas JM, Rifà J, Piñol JL, Real FX. Generalizing molecular results arising from incomplete biological samples: expected bias and unexpected findings. Ann Epidemiol. 2002;12:7–14.

    Article  PubMed  Google Scholar 

  74. Porta M, Malats N, Vioque J, Carrato C, Soler M, Ruiz L, Barberà V, Ayude D, Real FX. Incomplete overlapping of biological, clinical and environmental information in molecular epidemiologic studies: a variety of causes and a cascade of consequences. J Epidemiol Community Health. 2002;56:734–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Porta M, Pumarega J, Ferrer-Armengou O, López T, Alguacil J, Malats N, Fernàndez E. Timing of blood extraction in epidemiologic and proteomic studies: results and proposals from the PANKRAS II Study. Eur J Epidemiol. 2007;22:577–88.

    Article  PubMed  Google Scholar 

  76. Greaves MF. Aetiology of acute leukaemia. Lancet. 1997;349:344–9.

    Article  CAS  PubMed  Google Scholar 

  77. Schüz J, Ahlbom A. Exposure to electromagnetic fields and the risk of childhood leukaemia: a review. Radiat Prot Dosimetry. 2008;132:202–11.

    Article  PubMed  Google Scholar 

  78. Porta M, Greenland S, Hernán M, dos Santos Silva I, Last M, editors. A dictionary of epidemiology. 6th ed. New York: Oxford University Press; 2014.

    Google Scholar 

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

    Google Scholar 

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Acknowledgments

We thank María Téllez-Plaza and Roberto Pastor-Barriuso for helpful comments to earlier versions of the manuscript. The work was supported in part by research grants from Instituto de Salud Carlos III – FEDER (FIS PI13/00020 and CIBER de Epidemiología y Salud Pública – CIBERESP), Government of Spain; Fundació La Marató de TV3 (20132910); and Government of Catalonia (2014 SGR 1012).

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Authors and Affiliations

  1. Hospital del Mar Institute of Medical Research (IMIM), Faculty of Medicine, Universitat Autònoma de Barcelona, Carrer del Dr. Aiguader, 88, 08003, Barcelona, Catalonia, Spain

    Miquel Porta

  2. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA

    Miquel Porta

  3. CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

    Miquel Porta & Francisco Bolúmar

  4. Faculty of Medicine, University of Alcalá, Alcalá de Henares, Madrid, Spain

    Francisco Bolúmar

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  1. Miquel Porta
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Correspondence to Miquel Porta.

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Porta, M., Bolúmar, F. Caution: work in progress. Eur J Epidemiol 31, 535–539 (2016). https://doi.org/10.1007/s10654-016-0181-3

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