Publication
Title
Data-driven methods for imputing national-level incidence in global burden of disease studies
Author
Abstract
neoObjective To develop transparent and reproducible methods for imputing missing data on disease incidence at national-level for the year 2005. Methods We compared several models for imputing missing country-level incidence rates for two foodborne diseases congenital toxoplasmosis and aflatoxin-related hepatocellular carcinoma. Missing values were assumed to be missing at random. Predictor variables were selected using least absolute shrinkage and selection operator regression. We compared the predictive performance of naive extrapolation approaches and Bayesian random and mixed-effects regression models. Leave-one-out cross-validation was used to evaluate model accuracy. Findings The predictive accuracy of the Bayesian mixed-effects models was significantly better than that of the naive extrapolation method for one of the two disease models. However, Bayesian mixed-effects models produced wider prediction intervals for both data sets. Conclusion Several approaches are available for imputing missing data at national level. Strengths of a hierarchical regression approach for this type of task are the ability to derive estimates from other similar countries, transparency, computational efficiency and ease of interpretation. The inclusion of informative covariates may improve model performance, but results should be appraised carefully
Language
English
Source (journal)
Bulletin of the World Health Organization. - Genève
Publication
Genève : 2015
ISSN
0042-9686
DOI
10.2471/BLT.14.139972
Volume/pages
93 (2015) , p. 228-236
ISI
000353934500014
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 02.03.2017
Last edited 01.12.2022
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