Correcting for non-ignorable missingness in smoking trends
Kopra, J., Härkänen, T., Tolonen, H., & Karvanen, J. (2015). Correcting for non-ignorable missingness in smoking trends. Stat, 4(1), 1-14. https://doi.org/10.1002/sta4.73
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2015Copyright
© Wiley. This is an author's final draft version of an article whose final and definitive form has been published by Wiley. Published in this repository with the kind permission of the publisher.
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.
Publisher
John Wiley Sons LtdISSN Search the Publication Forum
2049-1573Keywords
NB.
Please see also: Correction: Correcting for non-ignorable missingness in smoking trends (2017), URL: http://dx.doi.org/10.1002/sta4.146
Please see also
http://dx.doi.org/10.1002/sta4.146Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/24584627
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