Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-25T01:23:54.128Z Has data issue: false hasContentIssue false
  • English
  • Français

Sources of confounding in life course epidemiology

Part of: DOHaD 2017 World Congress

Published online by Cambridge University Press:  16 August 2018

S. Santos ,
D. Zugna ,
C. Pizzi  and
L. Richiardi
S. Santos*
Affiliation:
The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, The Netherlands Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
D. Zugna
Affiliation:
Department of Medical Sciences, Cancer Epidemiology Unit, University of Turin and CPO-Piemonte, Turin, Italy
C. Pizzi
Affiliation:
Department of Medical Sciences, Cancer Epidemiology Unit, University of Turin and CPO-Piemonte, Turin, Italy
L. Richiardi
Affiliation:
Department of Medical Sciences, Cancer Epidemiology Unit, University of Turin and CPO-Piemonte, Turin, Italy
*
*Address for correspondence: S. Santos, The Generation R Study Group, Room Na-2908, Erasmus MC, University Medical Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands E-mail: s.dasilvasantos@erasmusmc.nl
Get access Rights & Permissions [Opens in a new window]

Abstract

In epidemiologic analytical studies, the primary goal is to obtain a valid and precise estimate of the effect of the exposure of interest on a given outcome in the population under study. A crucial source of violation of the internal validity of a study involves bias arising from confounding, which is always a challenge in observational research, including life course epidemiology. The increasingly popular approach of meta-analyzing individual participant data from several observational studies also brings up to discussion the problem of confounding when combining data from different populations. In this study, we review and discuss the most common sources of confounding in life course epidemiology: (i) confounding by indication, (ii) impact of baseline selection on confounding, (iii) time-varying confounding and (iv) mediator–outcome confounding. We also discuss the issue of addressing confounding in the context of an individual participant data meta-analysis.

Keywords

confoundinglife coursevalidity
Type
Review
Information
Journal of Developmental Origins of Health and Disease , Volume 10 , Issue 3 , June 2019 , pp. 299 - 305
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Kuh, D, Ben-Shlomo, Y, Lynch, J, Hallqvist, J, Power, C. Life course epidemiology. J Epidemiol Community Health. 2003; 57, 778–783.CrossRefGoogle ScholarPubMed
2. Ben-Shlomo, Y, Kuh, D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol. 2002; 31, 285–293.CrossRefGoogle ScholarPubMed
3. Riley, RD, Lambert, PC, Abo-Zaid, G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010; 340, c221.CrossRefGoogle ScholarPubMed
4. Rothman, KJ, Greenland, S, Lash, TL. Modern Epidemiology, 3rd edn, 2008. Lippincott Williams & Wilkins: Philadelphia, PA.Google Scholar
5. Greenland, S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003; 14, 300–306.CrossRefGoogle ScholarPubMed
6. Miettinen, OS. The need for randomization in the study of intended effects. Stat Med. 1983; 2, 267–271.CrossRefGoogle Scholar
7. Strom, BL. Pharmacoepidemiology, 3rd edn, 2000. Wiley: New York, NY.CrossRefGoogle Scholar
8. Joseph, KS, Mehrabadi, A, Lisonkova, S. Confounding by indication and related concepts. Curr Epidemiol Rep. 2014; 1, 1–8.CrossRefGoogle Scholar
9. van Meel, ER, den Dekker, HT, Elbert, NJ, et al. A population-based prospective cohort study examining the influence of early-life respiratory tract infections on school-age lung function and asthma. Thorax. 2018; 73, 167–173.CrossRefGoogle ScholarPubMed
10. McMahon, AD. Approaches to combat with confounding by indication in observational studies of intended drug effects. Pharmacoepidemiol Drug Saf. 2003; 12, 551–558.CrossRefGoogle ScholarPubMed
11. Garbe, E, Suissa, S. Pharmacoepidemiology. In Handbook of Epidemiology (eds. Ahrens W, Pigeot I), 2nd edn, 2014; pp. 1875–1925. Springer: New York.CrossRefGoogle Scholar
12. Popovic, M, Rusconi, F, Zugna, D, et al. Prenatal exposure to antibiotics and wheezing in infancy: a birth cohort study. Eur Respir J. 2016; 47, 810–817.CrossRefGoogle ScholarPubMed
13. Huybrechts, KF, Palmsten, K, Avorn, J, et al. Antidepressant use in pregnancy and the risk of cardiac defects. N Engl J Med. 2014; 370, 2397–2407.CrossRefGoogle ScholarPubMed
14. Horwitz, RI, Feinstein, AR. The problem of ‘protopathic bias’ in case-control studies. Am J Med. 1980; 68, 255–258.CrossRefGoogle Scholar
15. Salas, M, Hofman, A, Stricker, BH. Confounding by indication: an example of variation in the use of epidemiologic terminology. Am J Epidemiol. 1999; 149, 981–983.CrossRefGoogle ScholarPubMed
16. Hernan, MA, Hernandez-Diaz, S, Robins, JM. A structural approach to selection bias. Epidemiology. 2004; 15, 615–625.CrossRefGoogle ScholarPubMed
17. Pizzi, C, De Stavola, BL, Pearce, N, et al. Selection bias and patterns of confounding in cohort studies: the case of the NINFEA web-based birth cohort. J Epidemiol Community Health. 2012; 66, 976–981.CrossRefGoogle ScholarPubMed
18. Richiardi, L, Pizzi, C, Pearce, N. Commentary: representativeness is usually not necessary and often should be avoided. Int J Epidemiol. 2013; 42, 1018–1022.CrossRefGoogle Scholar
19. Rothman, KJ, Gallacher, JE, Hatch, EE. Why representativeness should be avoided. Int J Epidemiol. 2013; 42, 1012–1014.CrossRefGoogle ScholarPubMed
20. Keiding, N, Louis, TA. Perils and potentials of self-selected entry to epidemiological studies and surveys. J R Statist Soc A. 2016; 179, 319–376.CrossRefGoogle Scholar
21. Pizzi, C, De Stavola, B, Merletti, F, et al. Sample selection and validity of exposure-disease association estimates in cohort studies. J Epidemiol Community Health. 2011; 65, 407–411.CrossRefGoogle ScholarPubMed
22. Glymour, MM. Using causal diagrams to understand common problems in social epidemiology. In Methods in Social Epidemiology (ed. Jossey-Bass), 2006. pp. 393–428. Jossey-Bass: San Francisco, CA.Google Scholar
23. Ogburn, EL, VanderWeele, TJ. On the nondifferential misclassification of a binary confounder. Epidemiology 2012; 23, 433–439.CrossRefGoogle ScholarPubMed
24. Schisterman, EF, Cole, SR, Platt, RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009; 20, 488–495.CrossRefGoogle ScholarPubMed
25. Hernán, MA, Robins, JM. Causal Inference. 2017. Chapman & Hall/CRC: Boca Raton, FL.Google Scholar
26. Daniel, RM, Cousens, SN, De Stavola, BL, Kenward, MG, Sterne, JA. Methods for dealing with time-dependent confounding. Stat Med. 2013; 32, 1584–1618.CrossRefGoogle ScholarPubMed
27. Hernan, MA, Brumback, B, Robins, JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology. 2000; 11, 561–570.CrossRefGoogle ScholarPubMed
28. Robins, JM, Hernan, MA, Brumback, B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000; 11, 550–560.CrossRefGoogle ScholarPubMed
29. Naimi, AI, Cole, SR, Kennedy, EH. An introduction to g methods. Int J Epidemiol. 2017; 46, 756–762.Google ScholarPubMed
30. Taubman, SL, Robins, JM, Mittleman, MA, Hernan, MA. Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. Int J Epidemiol. 2009; 38, 1599–1611.CrossRefGoogle ScholarPubMed
31. Hernan, MA, Cole, SR, Margolick, J, Cohen, M, Robins, JM. Structural accelerated failure time models for survival analysis in studies with time-varying treatments. Pharmacoepidemiol Drug Saf. 2005; 14, 477–491.CrossRefGoogle ScholarPubMed
32. Baron, RM, Kenny, DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986; 51, 1173–1182.CrossRefGoogle ScholarPubMed
33. Blakely, T, McKenzie, S, Carter, K. Misclassification of the mediator matters when estimating indirect effects. J Epidemiol Community Health. 2013; 67, 458–466.CrossRefGoogle ScholarPubMed
34. Valeri, L, Vanderweele, TJ. The estimation of direct and indirect causal effects in the presence of misclassified binary mediator. Biostatistics. 2014; 15, 498–512.CrossRefGoogle ScholarPubMed
35. VanderWeele, TJ, Valeri, L, Ogburn, EL. The role of measurement error and misclassification in mediation analysis: mediation and measurement error. Epidemiology. 2012; 23, 561–564.CrossRefGoogle ScholarPubMed
36. Ogburn, EL, VanderWeele, TJ. Analytic results on the bias due to nondifferential misclassification of a binary mediator. Am J Epidemiol. 2012; 176, 555–561.CrossRefGoogle ScholarPubMed
37. Richiardi, L, Bellocco, R, Zugna, D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013; 42, 1511–1519.CrossRefGoogle ScholarPubMed
38. Cole, SR, Platt, RW, Schisterman, EF, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010; 39, 417–420.CrossRefGoogle ScholarPubMed
39. VanderWeele, TJ (ed.). Sensitivity analysis for mediation. In Explanation in Causal Inference: Methods for Mediation and Interaction, 2015; pp. 66–97. Oxford University Press: New York.Google Scholar
40. Petersen, ML, Sinisi, SE, van der Laan, MJ. Estimation of direct causal effects. Epidemiology. 2006; 17, 276–284.CrossRefGoogle ScholarPubMed
41. Pearl, J. Direct and indirect effects. Seventeenth Conference of Uncertainty in Artificial Intelligence, 2001. Morgan Kaufmann: San Francisco, CA.Google Scholar
42. Hernandez-Diaz, S, Schisterman, EF, Hernan, MA. The birth weight “paradox” uncovered? Am J Epidemiol. 2006; 164, 1115–1120.CrossRefGoogle ScholarPubMed
43. VanderWeele, TJ, Mumford, SL, Schisterman, EF. Conditioning on intermediates in perinatal epidemiology. Epidemiology. 2012; 23, 1–9.CrossRefGoogle ScholarPubMed
44. VanderWeele, TJ. Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology. 2010; 21, 540–551.CrossRefGoogle ScholarPubMed
45. Daniel, RM, De Stavola, B. gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula. STATA J. 2011; 11, 479–517.CrossRefGoogle Scholar
46. Debray, TP, Moons, KG, Abo-Zaid, GM, Koffijberg, H, Riley, RD. Individual participant data meta-analysis for a binary outcome: one-stage or two-stage? PLoS One. 2013; 8, e60650.CrossRefGoogle ScholarPubMed
47. Higgins, JP, Thompson, SG, Deeks, JJ, Altman, DG. Measuring inconsistency in meta-analyses. BMJ. 2003; 327, 557–560.CrossRefGoogle ScholarPubMed