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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

BACKGROUND: To assess the nonresponse rates in a questionnaire survey with respect to administrative register data, and to correct the bias statistically. METHODS: The Finnish Regional Health and Well-being Study (ATH) in 2010 was based on a national sample and several regional samples. Missing data...

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Autores principales: Härkänen, Tommi, Kaikkonen, Risto, Virtala, Esa, Koskinen, Seppo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4246429/
https://www.ncbi.nlm.nih.gov/pubmed/25373328
http://dx.doi.org/10.1186/1471-2458-14-1150
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author Härkänen, Tommi
Kaikkonen, Risto
Virtala, Esa
Koskinen, Seppo
author_facet Härkänen, Tommi
Kaikkonen, Risto
Virtala, Esa
Koskinen, Seppo
author_sort Härkänen, Tommi
collection PubMed
description BACKGROUND: To assess the nonresponse rates in a questionnaire survey with respect to administrative register data, and to correct the bias statistically. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: Our 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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spelling pubmed-42464292014-11-29 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 Härkänen, Tommi Kaikkonen, Risto Virtala, Esa Koskinen, Seppo BMC Public Health Research Article BACKGROUND: To assess the nonresponse rates in a questionnaire survey with respect to administrative register data, and to correct the bias statistically. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: Our 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. BioMed Central 2014-11-06 /pmc/articles/PMC4246429/ /pubmed/25373328 http://dx.doi.org/10.1186/1471-2458-14-1150 Text en © Härkänen et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Härkänen, Tommi
Kaikkonen, Risto
Virtala, Esa
Koskinen, Seppo
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4246429/
https://www.ncbi.nlm.nih.gov/pubmed/25373328
http://dx.doi.org/10.1186/1471-2458-14-1150
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