  Data missing not at random (MNAR) is a major challenge in survey sampling. We
propose an approach based on registry data to deal with non-ignorable
missingness in health examination surveys. The approach relies on follow-up
data available from administrative registers several years after the survey.
For illustration we use data on smoking prevalence in Finnish National FINRISK
study conducted in 1972-1997. The data consist of measured survey information
including missingness indicators, register-based background information and
register-based time-to-disease survival data. The parameters of missingness
mechanism are estimable with these data although the original survey data are
MNAR. The underlying data generation process is modelled by a Bayesian model.
The results indicate that the estimated smoking prevalence rates in Finland may
be significantly affected by missing data.
