Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1782346511538978816 |
---|---|
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. |
format | Online Article Text |
id | pubmed-4246429 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT harkanentommi inverseprobabilityweightinganddoublyrobustmethodsincorrectingtheeffectsofnonresponseinthereimbursedmedicationandselfreportedturnoutestimatesintheathsurvey AT kaikkonenristo inverseprobabilityweightinganddoublyrobustmethodsincorrectingtheeffectsofnonresponseinthereimbursedmedicationandselfreportedturnoutestimatesintheathsurvey AT virtalaesa inverseprobabilityweightinganddoublyrobustmethodsincorrectingtheeffectsofnonresponseinthereimbursedmedicationandselfreportedturnoutestimatesintheathsurvey AT koskinenseppo inverseprobabilityweightinganddoublyrobustmethodsincorrectingtheeffectsofnonresponseinthereimbursedmedicationandselfreportedturnoutestimatesintheathsurvey |