Selection bias and patterns of confounding in cohort studies: the case of the NINFEA web-based birth cohort

J Epidemiol Community Health. 2012 Nov;66(11):976-81. doi: 10.1136/jech-2011-200065. Epub 2011 Dec 6.


Background: Several studies have examined the effects of sample selection on the exposure-outcome association estimates in cohort studies, but the reasons why this selection may induce bias have not been fully explored.

Aims: To investigate how sample selection of the web-based NINFEA birth cohort may change the confounding patterns present in the source population.

Methods: The characteristics of the NINFEA participants (n=1105) were compared with those of the wider source population-the Piedmont Birth Registry (PBR)-(n=36 092), and the association of two exposures (parity and educational level) with two outcomes (low birth weight and birth by caesarean section), while controlling for other risk factors, was studied. Specifically the associations among measured risk factors within each dataset were examined and the exposure-outcome estimates compared in terms of relative ORs.

Results: The associations of educational level with the other risk factors (alcohol consumption, folic acid intake, maternal age, pregnancy weight gain, previous miscarriages) partly differed between PBR and NINFEA. This was not observed for parity. Overall, the exposure-outcome estimates derived from NINFEA only differed moderately from those obtained in PBR, with relative ORs ranging between 0.74 and 1.03.

Conclusions: Sample selection in cohort studies may alter the confounding patterns originally present in the general population. However, this does not necessarily introduce selection bias in the exposure-outcome estimates, as sample selection may reduce some of the residual confounding present in the general population.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cohort Studies*
  • Confounding Factors, Epidemiologic
  • Female
  • Humans
  • Infant, Newborn
  • Internet
  • Patient Selection*
  • Pregnancy
  • Research Design
  • Risk Factors
  • Selection Bias*