The problem of attrition in a Finnish longitudinal survey on depression

A cohort of all school children aged 16 years in 1983 (n = 2194, 96.7%) in Tampere, Finland were studied at 16, 22 and 32 years of age by self-reported questionnaires. The non-response pattern was considered by modelling the individual response probability by panel year and gender. Gender and school performance at age 16 years were the most important predictors of non-response. They explained away the effect of all other variables at 16 and 22 years, except for earlier non-response at age 22. However, the ability of the models to predict non-respondents was very poor. The effect of attrition for the estimation of depression prevalence was evaluated first by longitudinal weighting methods used commonly in survey studies and then by Markov chain Monte Carlo (MCMC) simulation of the missing depression status. Under the missing-at-random assumption (MAR), both applied correction methods gave estimates of roughly the same size and did not significantly differ from the observed prevalence of depression. No indication of informative missingness was found. We therefore conclude that attrition does not seriously bias the estimation of depression prevalence in the data. In general, non-response models, which are needed to correct for informative missingness, are likely to have poor ability to predict non-response. Therefore, the plausibility of the MAR assumption is important in the presence of attrition.