ArticleBMC public health2014
Inverse probability weighting and doubly robust methods in correcting the effects of non-response in the reimbursed medication and self-reported turnout estimates in the ATH survey.
Article in BMC public health, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed.
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- Pairwise association of key lifestyle factors and risk of solid cancers - A prospective pooled multi-cohort register study.Preventive medicine reports · 2024Article
- Predicting Internet Use and Digital Competence Among Older Adults Using Performance Tests of Visual, Physical, and Cognitive Functioning: Longitudinal Population-Based Study.Journal of medical Internet research · 2023Article
- Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults.PLOS digital health · 2023Article
- Austerity, economic hardship and access to medications: a repeated cross-sectional population survey study, 2013-2020.Journal of epidemiology and community health · 2023Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo assess the nonresponse rates in a questionnaire survey with respect to administrative register data, and to correct the bias statistically.
methodsThe Finnish Regional Health and Well-being Study (ATH) in 2010 was based on a national sample and several regional samples. Missing data analysis was based on socio-demographic register data covering the whole sample. Inverse probability weighting (IPW) and doubly robust (DR) methods were estimated using the logistic regression model, which was selected using the Bayesian information criteria. The crude, weighted and true self-reported turnout in the 2008 municipal election and prevalences of entitlements to specially reimbursed medication, and the crude and weighted body mass index (BMI) means were compared.
resultsThe IPW method appeared to remove a relatively large proportion of the bias compared to the crude prevalence estimates of the turnout and the entitlements to specially reimbursed medication. Several demographic factors were shown to be associated with missing data, but few interactions were found.
conclusionsOur results suggest that the IPW method can improve the accuracy of results of a population survey, and the model selection provides insight into the structure of missing data. However, health-related missing data mechanisms are beyond the scope of statistical methods, which mainly rely on socio-demographic information to correct the results.
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