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March 2016 Prevalence and trend estimation from observational data with highly variable post-stratification weights
Yannick Vandendijck, Christel Faes, Niel Hens
Ann. Appl. Stat. 10(1): 94-117 (March 2016). DOI: 10.1214/15-AOAS874


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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Yannick Vandendijck. Christel Faes. Niel Hens. "Prevalence and trend estimation from observational data with highly variable post-stratification weights." Ann. Appl. Stat. 10 (1) 94 - 117, March 2016.


Received: 1 December 2014; Revised: 1 July 2015; Published: March 2016
First available in Project Euclid: 25 March 2016

zbMATH: 06586138
MathSciNet: MR3480489
Digital Object Identifier: 10.1214/15-AOAS874

Keywords: Binary data , empirical Bayes estimation , influenza-like illness , Nonparametric regression , observational survey , post-stratification , random-effects model

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 1 • March 2016
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