Differential loss of participants does not necessarily cause selection bias

Aust N Z J Public Health. 2012 Jun;36(3):218-22. doi: 10.1111/j.1753-6405.2012.00867.x.

Abstract

Background: Most research is affected by differential participation, where individuals who do not participate have different characteristics to those who do. This is often assumed to induce selection bias. However, selection bias only occurs if the exposure-outcome association differs for participants compared to non-participants. We empirically demonstrate that selection bias does not necessarily occur when participation varies in a study.

Methods: We used data from three waves of the longitudinal Survey of Family, Income and Employment (SoFIE). We examined baseline associations of labour market activity and education with self-rated health using logistic regression in five participation samples: A) the original sample at year one (n=22,260); B) those remaining in the sample (n=18,360); C) those (at year 3) consenting to data linkage (n=14,350); D) drop outs over three years (n=3,895); and E) those who dropped out or did not consent (n=7,905).

Results: Loss to follow-up was more likely among lower socioeconomic groups and those with poorer health. However, for labour market activity and education, the odds of reporting fair/poor health were similar across all samples. Comparisons of the mutually exclusive samples (C and E) showed no difference in the odds ratios after adjustment for sociodemographic (participation) variables. Thus, there was little evidence of selection bias.

Conclusions: Differential loss to follow-up (drop out) need not lead to selection bias in the association between exposure (labour market activity and education) and outcome (self-rated health).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Data Collection
  • Educational Status
  • Employment / statistics & numerical data*
  • Health Status*
  • Humans
  • Income
  • Logistic Models
  • Longitudinal Studies
  • Odds Ratio
  • Patient Dropouts / statistics & numerical data*
  • Selection Bias*
  • Socioeconomic Factors