Skip to main content Accessibility help

Sources of confounding in life course epidemiology

  • S. Santos (a1) (a2), D. Zugna (a3), C. Pizzi (a3) and L. Richiardi (a3)


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.


Corresponding author

*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:


Hide All
1. Kuh, D, Ben-Shlomo, Y, Lynch, J, Hallqvist, J, Power, C. Life course epidemiology. J Epidemiol Community Health. 2003; 57, 778783.
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, 285293.
3. Riley, RD, Lambert, PC, Abo-Zaid, G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010; 340, c221.
4. Rothman, KJ, Greenland, S, Lash, TL. Modern Epidemiology, 3rd edn, 2008. Lippincott Williams & Wilkins: Philadelphia, PA.
5. Greenland, S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003; 14, 300306.
6. Miettinen, OS. The need for randomization in the study of intended effects. Stat Med. 1983; 2, 267271.
7. Strom, BL. Pharmacoepidemiology, 3rd edn, 2000. Wiley: New York, NY.
8. Joseph, KS, Mehrabadi, A, Lisonkova, S. Confounding by indication and related concepts. Curr Epidemiol Rep. 2014; 1, 18.
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, 167173.
10. McMahon, AD. Approaches to combat with confounding by indication in observational studies of intended drug effects. Pharmacoepidemiol Drug Saf. 2003; 12, 551558.
11. Garbe, E, Suissa, S. Pharmacoepidemiology. In Handbook of Epidemiology (eds. Ahrens W, Pigeot I), 2nd edn, 2014; pp. 1875–1925. Springer: New York.
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, 810817.
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, 23972407.
14. Horwitz, RI, Feinstein, AR. The problem of ‘protopathic bias’ in case-control studies. Am J Med. 1980; 68, 255258.
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, 981983.
16. Hernan, MA, Hernandez-Diaz, S, Robins, JM. A structural approach to selection bias. Epidemiology. 2004; 15, 615625.
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, 976981.
18. Richiardi, L, Pizzi, C, Pearce, N. Commentary: representativeness is usually not necessary and often should be avoided. Int J Epidemiol. 2013; 42, 10181022.
19. Rothman, KJ, Gallacher, JE, Hatch, EE. Why representativeness should be avoided. Int J Epidemiol. 2013; 42, 10121014.
20. Keiding, N, Louis, TA. Perils and potentials of self-selected entry to epidemiological studies and surveys. J R Statist Soc A. 2016; 179, 319376.
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, 407411.
22. Glymour, MM. Using causal diagrams to understand common problems in social epidemiology. In Methods in Social Epidemiology (ed. Jossey-Bass), 2006. pp. 393428. Jossey-Bass: San Francisco, CA.
23. Ogburn, EL, VanderWeele, TJ. On the nondifferential misclassification of a binary confounder. Epidemiology 2012; 23, 433439.
24. Schisterman, EF, Cole, SR, Platt, RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009; 20, 488495.
25. Hernán, MA, Robins, JM. Causal Inference. 2017. Chapman & Hall/CRC: Boca Raton, FL.
26. Daniel, RM, Cousens, SN, De Stavola, BL, Kenward, MG, Sterne, JA. Methods for dealing with time-dependent confounding. Stat Med. 2013; 32, 15841618.
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, 561570.
28. Robins, JM, Hernan, MA, Brumback, B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000; 11, 550560.
29. Naimi, AI, Cole, SR, Kennedy, EH. An introduction to g methods. Int J Epidemiol. 2017; 46, 756762.
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, 15991611.
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, 477491.
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, 11731182.
33. Blakely, T, McKenzie, S, Carter, K. Misclassification of the mediator matters when estimating indirect effects. J Epidemiol Community Health. 2013; 67, 458466.
34. Valeri, L, Vanderweele, TJ. The estimation of direct and indirect causal effects in the presence of misclassified binary mediator. Biostatistics. 2014; 15, 498512.
35. VanderWeele, TJ, Valeri, L, Ogburn, EL. The role of measurement error and misclassification in mediation analysis: mediation and measurement error. Epidemiology. 2012; 23, 561564.
36. Ogburn, EL, VanderWeele, TJ. Analytic results on the bias due to nondifferential misclassification of a binary mediator. Am J Epidemiol. 2012; 176, 555561.
37. Richiardi, L, Bellocco, R, Zugna, D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013; 42, 15111519.
38. Cole, SR, Platt, RW, Schisterman, EF, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010; 39, 417420.
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.
40. Petersen, ML, Sinisi, SE, van der Laan, MJ. Estimation of direct causal effects. Epidemiology. 2006; 17, 276284.
41. Pearl, J. Direct and indirect effects. Seventeenth Conference of Uncertainty in Artificial Intelligence, 2001. Morgan Kaufmann: San Francisco, CA.
42. Hernandez-Diaz, S, Schisterman, EF, Hernan, MA. The birth weight “paradox” uncovered? Am J Epidemiol. 2006; 164, 11151120.
43. VanderWeele, TJ, Mumford, SL, Schisterman, EF. Conditioning on intermediates in perinatal epidemiology. Epidemiology. 2012; 23, 19.
44. VanderWeele, TJ. Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology. 2010; 21, 540551.
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, 479517.
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.
47. Higgins, JP, Thompson, SG, Deeks, JJ, Altman, DG. Measuring inconsistency in meta-analyses. BMJ. 2003; 327, 557560.


Related content

Powered by UNSILO

Sources of confounding in life course epidemiology

  • S. Santos (a1) (a2), D. Zugna (a3), C. Pizzi (a3) and L. Richiardi (a3)


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.