Characterising attrition from childhood to adulthood in a 20-year cohort: which baseline factors are influential, and can bias be corrected?
Otahal, P and Blizzard, L and Hosmer, DM and Stankovich, J and Venn, A, Characterising attrition from childhood to adulthood in a 20-year cohort: which baseline factors are influential, and can bias be corrected?, Longitudinal and Life Course Studies pp. 1-14. ISSN 1757-9597 (2021) [Refereed Article]
Attrition is common in longitudinal studies and can lead to bias when the missingness pattern affects the distributions of analysed variables. Characterisation of factors predictive of attrition is vital to longitudinal research. Few studies have investigated the factors predictive of attrition from childhood cohorts with large-scale loss to follow-up. Methods to remove potential bias are available and have been well studied in scenarios of short intervening periods between contact and follow-up. Less is known about the performance of such techniques when there is a large initial loss of participants after a long intervening period. The Australian Schools Health and Fitness Survey (ASHFS) was conducted in 1985 when participants were school children aged 7–15 years. The first follow-up occurred 20 years later with substantial loss of participants: 80% were traced, 61% enrolled and provided brief questionnaire information, 47% provided more extensive questionnaire information and 28% attended clinics. Factors associated with attrition were examined and two common techniques, multiple imputation (MI) and inverse probability weighting (IPW) were used to determine the potential for correcting the bias in the estimate of the association between self-rated fitness and BMI in childhood. Attrition from childhood to adulthood was found to be influenced by the same factors that operate in adult cohorts: lower education, lower socio-economic position and male sex. Attrition patterns varied by the stage of follow-up. Estimated childhood associations biased by adulthood attrition were able to be corrected using MI, but IPW was unsuccessful due to a lack of completely observed informative variables.
attrition, bias, follow-up, imputation, inverse probability weighting, validity