6533b7dbfe1ef96bd12709db
RESEARCH PRODUCT
Bayesian models for data missing not at random in health examination surveys
Juho KopraTommi HärkänenJuha Karvanensubject
Statistics and ProbabilityFOS: Computer and information sciencesmedicine.medical_specialtymultiple imputationComputer scienceBayesian probability01 natural sciencesStatistics - Applicationssurvival analysisfollow-up dataMethodology (stat.ME)010104 statistics & probability03 medical and health sciencesHealth examination0302 clinical medicineEpidemiologyStatisticsmedicineApplications (stat.AP)030212 general & internal medicine0101 mathematicsSurvival analysisStatistics - MethodologyBayes estimatorta112elinaika-analyysiRisk factor (computing)Bayesian estimation3. Good healthhealth examination surveysStatistics Probability and UncertaintyMissing not at randomdata augmentationdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-01 |