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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21524

Title: Prevalence and trend estimation from observational data with highly variable post-stratification weights
Authors: Vandendijck, Yannick
Faes, Christel
Hens, Niel
Issue Date: 2016
Citation: ANNALS OF APPLIED STATISTICS, 10 (1), p. 94-117
Abstract: 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.
Notes: Vandendijck, Y (reprint author), Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. yannick.vandendijck@uhasselt.be; christel.faes@uhasselt.be; niel.hens@uhasselt.be
URI: http://hdl.handle.net/1942/21524
Link to publication: http://projecteuclid.org/euclid.aoas/1458909909
DOI: 10.1214/15-AOAS874
ISI #: 000378116900005
ISSN: 1932-6157
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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