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

BMC Public Health

Research Article

Esa Virtala¹  Risto Kaikkonen¹  Seppo Koskinen¹  Tommi Härkänen¹ 

[1] Department of Health, Functional Capacity and Welfare National Institute for Health and Welfare (THL), P.O. Box 30, FI-00271, Helsinki, Finland;

关键词: Missing data;    Population survey;    Register data;    Model selection;    Inverse probability weighting;    Doubly robust methods;   

DOI  :  10.1186/1471-2458-14-1150

 received in 2014-05-12, accepted in 2014-10-24,  发布年份 2014

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Härkänen et al.; licensee BioMed Central Ltd. 2014