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BACKGROUND: In observational research, choosing an optimal analysis strategy when variables are incomplete requires an understanding of the factors associated with ongoing participation and non-response, but this cannot be fully examined with incomplete data. Linkage to external datasets provides additional information on those with incomplete data, allowing examination of factors related to missingness. METHODS: We examined the association between baseline sociodemographic factors and ongoing participation in the Avon Longitudinal Study of Parents and Children. We investigated whether child and adolescent outcomes measured in linked education and primary care data were associated with participation, after accounting for baseline factors. To demonstrate the potential for bias, we examined whether the association between maternal smoking and these outcomes differed in the subsample who completed the 19-year questionnaire. RESULTS: Lower levels of school attainment, lower general practitioner (GP) consultation and prescription rates, higher body mass index (BMI), special educational needs (SEN) status, not having an asthma diagnosis, depression and being a smoker were associated with lower participation after adjustment for baseline factors. For example, the adjusted odds ratio (OR) for participation comparing ever smokers (by 18 years) with non-smokers was: 0.65, 95% CI (0.56, 0.75). The associations with maternal smoking differed between the subsample of participants at 19 years and the entire sample, although differences were small and confidence intervals overlapped. For example: for SEN status, OR = 1.19 (1.06, 1.33) (all participants); OR = 1.03 (0.79, 1.45) (subsample). CONCLUSIONS: A range of health-related and educational factors are associated with ongoing participation in ALSPAC; this is likely to be the case in other cohort studies. Researchers need to be aware of this when planning their analysis. Cohort studies can use linkage to routine data to explore predictors of ongoing participation and conduct sensitivity analyses to assess potential bias.
Cornish et al. (Thu,) studied this question.