Skip Navigation


Biostatistics Advance Access originally published online on March 18, 2008
Biostatistics 2008 9(4):715-734; doi:10.1093/biostatistics/kxn004
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Supplementary Material
Right arrow All Versions of this Article:
9/4/715    most recent
kxn004v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Sweeting, M. J.
Right arrow Articles by Ades, A. E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Sweeting, M. J.
Right arrow Articles by Ades, A. E.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Estimating hepatitis C prevalence in England and Wales by synthesizing evidence from multiple data sources. Assessing data conflict and model fit

M. J. Sweeting*

MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK michael.sweeting{at}mrc-bsu.cam.ac.uk

D. De Angelis

MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK and Health Protection Agency Centre for Infections, Statistics Unit, 61 Colindale Avenue, London NW9 5EQ, UK

M. Hickman

Department of Social Medicine, University of Bristol, Canynge Hall, Whiteladies Road, Bristol BS8 2SP, UK

A. E. Ades

MRC Health Services Research Collaboration, University of Bristol, Canynge Hall, Whiteladies Road, Bristol BS8 2PR, UK

* To whom correspondence should be addressed.

Multiparameter evidence synthesis is becoming widely used as a way of combining evidence from multiple and often disparate sources of information concerning a number of parameters. Synthesizing data in one encompassing model allows propagation of evidence and learning. We demonstrate the use of such an approach in estimating the number of people infected with the hepatitis C virus (HCV) in England and Wales. Data are obtained from seroprevalence studies conducted in different subpopulations. Each subpopulation is modeled as a composition of 3 main HCV risk groups (current injecting drug users (IDUs), ex-IDUs, and non-IDUs). Further, data obtained on the prevalence (size) of each risk group provide an estimate of the prevalence of HCV in the whole population. We simultaneously estimate all model parameters through the use of Bayesian Markov chain Monte Carlo techniques. The main emphasis of this paper is the assessment of evidence consistency and investigation of the main drivers for model inferences. We consider a cross-validation technique to reveal data conflict and leverage when each data source is in turn removed from the model.

Keywords: Cross-validation; Evidence synthesis; Goodness-of-fit; Hepatitis C; Prevalence

Received March 9, 2007; revised December 21, 2007; accepted for publication January 22, 2008.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.