Title
Prevalence and trend estimation from observational data with highly variable post-stratification weights
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
article
Publication
Cleveland, Ohio ,
Subject
Mathematics
Source (journal)
Annals of applied statistics. - Cleveland, Ohio
Volume/pages
10(2016) :1 , p. 94-117
ISSN
1932-6157
ISI
000378116900005
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/7452b1/134636.pdf
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