  In epidemiological surveys, data missing not at random (MNAR) due to survey
nonresponse may potentially lead to a bias in the risk factor estimates. We
propose an approach based on Bayesian data augmentation and survival modelling
to reduce the nonresponse bias. The approach requires additional information
based on follow-up data. We present a case study of smoking prevalence using
FINRISK data collected between 1972 and 2007 with a follow-up to the end of
2012 and compare it to other commonly applied missing at random (MAR)
imputation approaches. A simulation experiment is carried out to study the
validity of the approaches. Our approach appears to reduce the nonresponse bias
substantially, where as MAR imputation was not successful in bias reduction.
