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Title
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Prevalence and trend estimation from observational data with highly variable post-stratification weights
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Author
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Abstract
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| In observational surveys, post-stratification is used to reduce bias resulting from differences between the survey population and the population under investigation. However, this can lead to inflated post-stratification weights and, therefore, appropriate methods are required to obtain less variable estimates. Proposed methods include collapsing post-strata, trimming post-stratification weights, generalized regression estimators (GREG) and weight smoothing models, the latter defined by random-effects models that induce shrinkage across post-stratum means. Here, we first describe the weight-smoothing model for prevalence estimation from binary survey outcomes in observational surveys. Second, we propose an extension of this method for trend estimation. And, third, a method is provided such that the GREG can be used for prevalence and trend estimation for observational surveys. Variance estimates of all methods are described. A simulation study is performed to compare the proposed methods with other established methods. The performance of the nonparametric GREG is consistent over all simulation conditions and therefore serves as a valuable solution for prevalence and trend estimation from observational surveys. The method is applied to the estimation of the prevalence and incidence trend of influenza-like illness using the 2010/2011 Great Influenza Survey in Flanders, Belgium. |
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Language
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English
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Source (journal)
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Annals of applied statistics. - Cleveland, Ohio
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Publication
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Cleveland, Ohio
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2016
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ISSN
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1932-6157
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DOI
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10.1214/15-AOAS874
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Volume/pages
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10
:1
(2016)
, p. 94-117
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ISI
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000378116900005
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Full text (Publisher's DOI)
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Full text (open access)
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