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.
Ahern, K. and Le Brocque, R. (2005) Methodological issues in the effects of attrition: simple solutions for social scientists, Field Methods, 17(1): 53–69.
Badawi, M.A., Eaton, W.W., Myllyluoma, J., Weimer, L.G. and Gallo, J. (1999) Psychopathology and attrition in the Baltimore ECA 15-year follow-up 1981–1996, Social Psychiatry and Psychiatric Epidemiology, 34(2): 91–8.
Brick, J.M. (2013) Unit nonresponse and weighting adjustments: a critical review, Journal of Official Statistics, 29(3): 329–53.
Burkam, D.T. and Lee, V.E. (1998) Effects of monotone and nonmonotone attrition on parameter estimates in regression models with educational data: demographic effects on achievement, aspirations, and attitudes, The Journal of Human Resources, 33(2): 555–74.
Cole, T.J., Flegal, K.M., Nicholls, D. and Jackson, A.A. (2007) Body mass index cut offs to define thinness in children and adolescents: international survey, British Medical Journal, 335(7612): 194–7.
Cumming, J.J. and Goldstein, H. (2016) Handling attrition and non-response in longitudinal data with an application to a study of Australian youth, Longitudinal and Life Course Studies, 7(1): 53–63.
Doidge, J.C. (2018) Responsiveness-informed multiple imputation and inverse probability-weighting in cohort studies with missing data that are non-monotone or not missing at random, Statistical Methods in Medical Research, 27(2): 352–63.
Eerola, M., Huurre, T. and Aro, H. (2005) The problem of attrition in a Finnish longitudinal survey on depression, European Journal of Epidemiology, 20(1): 113–20.
Fitzgerald, J., Gottschalk, P. and Moffitt, R. (1998) An analysis of sample attrition in panel data: the Michigan panel study of income dynamics, The Journal of Human Resources, 33(2): 251–99.
Fröjd, S.A., Kaltiala-Heino, R. and Marttunen, M.J. (2010) Does problem behaviour affect attrition from a cohort study on adolescent mental health?, European Journal of Public Health, 21(3): 306–10.
Greenland, S. (1977) Response and follow-up bias in cohort studies, American Journal of Epidemiology, 106(3): 184–7.
Gustavson, K., von Soest, T., Karevold, E. and Røysamb, E. (2012) Attrition and generalizability in longitudinal studies: findings from a 15-year population-based study and a Monte Carlo simulation study, BMC Public Health, 12(1): art. 918, https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-12-918
Mostafa, T. and Wiggins, R. (2015) The impact of attrition and non-response in birth cohort studies: a need to incorporate missingness strategies, Longitudinal and Life Course Studies, 6(2): 131–46.
Olsen, R.J. (2005) The problem of respondent attrition: survey methodology is key, Monthly Labor Review, 128(2): 63–70.
Pyke, J.E. (1987) Australian Health and Fitness Survey 1985, Parkside: The Australian Council for Health, Physical Education and Recreation Inc.
R Core Team (2019) R: A Language and Environment for Statistical Computing, Vienna: R Foundation for Statistical Computing, https://www.R-project.org/
Rubin, D.B. (1976) Inference and missing data, Biometrika, 63(3): 581–92.
Seaman, S.R. and White, I.R. (2013) Review of inverse probability weighting for dealing with missing data, Statistical Methods in Medical Research, 22(3): 278–95.
Siddiqui, O., Flay, B.R. and Hu, F.B. (1996) Factors affecting attrition in a longitudinal smoking prevention study, Preventive Medicine, 25(5): 554–60.
United Directory Systems (2018) Australia on Disc, https://www.australiaondisc.com/
van Buuren, S. (2012) Flexible Imputation of Missing Data, Boca Raton, FL: CRC Press.
van Buuren, S. and Groothuis-Oudshoorn, K. (2011) mice: multivariate imputation by chained equations in R, Journal of Statistical Software, 45(3), https://www.jstatsoft.org/article/view/v045i03
Young, A.F., Powers, J.R. and Bell, S.L. (2006) Attrition in longitudinal studies: who do you lose?, Australian and New Zealand Journal of Public Health, 30(4): 353–61.
May 2022 onwards | Past Year | Past 30 Days | |
---|---|---|---|
Abstract Views | 116 | 110 | 3 |
Full Text Views | 14 | 5 | 0 |
PDF Downloads | 8 | 5 | 0 |