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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 | Source: | 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 | Keywords: | Binary data; empirical Bayes estimation; influenza-like illness; nonparametric regression; observational survey; post-stratification; random-effects model | Document URI: | http://hdl.handle.net/1942/21524 | Link to publication/dataset: | http://projecteuclid.org/euclid.aoas/1458909909 | ISSN: | 1932-6157 | e-ISSN: | 1941-7330 | DOI: | 10.1214/15-AOAS874 | ISI #: | 000378116900005 | Rights: | © Institute of Mathematical Statistics, 2016 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2017 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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AOAS874 (1).pdf Restricted Access | Published version | 382.52 kB | Adobe PDF | View/Open Request a copy |
ims_SupplementaryMaterials_YV_CF_NH_Accepted.pdf Restricted Access | Supplementary material | 13.37 MB | Adobe PDF | View/Open Request a copy |
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