Bulletin of the World Health Organization · 2015
Data-driven methods for imputing national-level incidence in global burden of disease studies.
Сравнительное исследованиеСтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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McDonald Scott A, Devleesschauwer Brecht, Speybroeck Niko, Hens Niel, Praet Nicolas, Torgerson Paul R, Havelaar Arie H, Wu Felicia, Tremblay Marlène, Amene Ermias W, Döpfer Dörte
Аннотация
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.
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.