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

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.

Consumption of healthy foods and associated socio-demographic factors among Russian, Somali and Kurdish immigrants in Finland

“…Different sampling probabilities and non-response were handled using the inverse probability weights based on register information on age, sex, immigrant group, study location, and marital status. In addition, sampling design was taken into account through finite population correction in all analyses [16,17]. Data were analysed separately for each immigrant group.…”

Spatial nature of urban well-being

“…As is usual with a questionnaire survey data, nonresponse bias is a problem that was corrected by weighting. Here, inverse probability weighting (IPW) corrects the effects of non-response (Härkänen, Kaikkonen, Virtala, & Koskinen, 2014). Besides weights, finite population correction (FPC) (Lehtonen & Pahkinen, 2004) was applied in all analyses.…”

Factors Contributing to Psychological Distress in the Working Population, With A Special Reference to Gender Difference - A Population-Based Study

“…This cross-sectional study used data from the ATH Study [74]. The ATH was a questionnaire survey of health, well-being and service use targeted at the population of Finland aged 20 years or over, implemented annually from 2010 to 2016.…”

“…The ATH was a questionnaire survey of health, well-being and service use targeted at the population of Finland aged 20 years or over, implemented annually from 2010 to 2016. A strati ed random sampling design, described in detail by Härkänen et al [74], was used, and the sampling was done without replacement. Inverse probability weighting was used to account for missing data [74].…”

“…A strati ed random sampling design, described in detail by Härkänen et al [74], was used, and the sampling was done without replacement. Inverse probability weighting was used to account for missing data [74]. Data used in the present study are from nationally representative samples collected in the years 2012-2016 (n = 96 668).…”