Data-driven methods for imputing national-level incidence in global burden of disease studies.
Objective 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-rela...
| Publicado en: | Bulletin of the World Health Organization Vol. 93; no. 4; pp. 228 - 237 |
|---|---|
| Autores principales: | , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
World Health Organization
Apr2015
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103796244&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103796244 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00429686 BUW jtl: Bulletin of the World Health Organization issn: 00429686 maglogo: N pubinfo: dt: Apr2015 vid: 93 iid: 4 pid: 437 pub: World Health Organization artinfo: ui: 103796244 102417851 10.2471/BLT.14.139972 103796244 ppf: 228 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Data-driven methods for imputing national-level incidence in global burden of disease studies. aug: au: McDonald, Scott A. Devleesschauwer, Brecht Speybroeck, Niko Hens, Niel Praet, Nicolas Torgerson, Paul R. Havelaar, Arie H. Wu, Felicia Tremblay, Marlène W Amene, Ermias Döpfer, Dörte affil: Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands sug: subj: Research Methodology Research Measurement Methods Data Analysis, Statistical Methods Disease Surveillance Incidence Comparative Studies Confidence Intervals Regression Funding Source ab: Objective 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|