A hierarchical framework for correcting under-reporting in count data

Stoner, O. , Economou, T. and Drummond Marques da Silva, G. (2019) A hierarchical framework for correcting under-reporting in count data. Journal of the American Statistical Association, 114(528), pp. 1481-1492. (doi: 10.1080/01621459.2019.1573732)

[img] Text
249540.pdf - Published Version
Available under License Creative Commons Attribution.

6MB

Abstract

Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention. We present a comprehensive investigation and application of a Bayesian hierarchical approach to modeling and correcting under-reporting in tuberculosis counts, a general problem arising in observational count data. The framework is applicable to fully under-reported data, relying only on an informative prior distribution for the mean reporting rate to supplement the partial information in the data. Covariates are used to inform both the true count-generating process and the under-reporting mechanism, while also allowing for complex spatio-temporal structures. We present several sensitivity analyses based on simulation experiments to aid the elicitation of the prior distribution for the mean reporting rate and decisions relating to the inclusion of covariates. Both prior and posterior predictive model checking are presented, as well as a critical evaluation of the approach. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Item Type:Articles
Additional Information:The authors gratefully acknowledge the funding of this research in part by the Engineering and Physical Sciences Research Council (EPSRC) and in part by a GW4+ Doctoral Training Partnership Studentship from the Natural Environment Research Council [NE/L002434/1].
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Stoner, Dr Oliver
Authors: Stoner, O., Economou, T., and Drummond Marques da Silva, G.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of the American Statistical Association
Publisher:Taylor & Francis
ISSN:0162-1459
ISSN (Online):1537-274X
Published Online:09 March 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Journal of the American Statistical Association 114(528) 1481-1492
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record