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Conflicting evidence in a Bayesian synthesis of surveillance data to estimate human immunodeficiency virus prevalence

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  • A. M. Presanis
  • D. De Angelis
  • D. J. Spiegelhalter
  • S. Seaman
  • A. Goubar
  • A. E. Ades

Abstract

Summary. Inferential approaches based on the synthesis of diverse sources of evidence are increasingly employed in epidemiology as a means of exploiting all available information, perhaps from studies of differing designs. The application of the synthesis of evidence to real world problems generally leads to the formulation of probability models which are highly complex and for which there is a clear need for a well‐defined iterative process of model criticism. This process should include an appraisal of model fit and the detection of inconsistent or conflicting evidence. The latter is especially relevant as, typically, multiple sources of data provide information on the same parameter. Detected conflicts need then to be resolved. We present a case‐study of the detection and resolution of conflicting evidence, using as an illustration the estimation of the prevalence of human immunodeficiency virus (HIV) infection in England and Wales. We employ a Bayesian model to synthesize routine surveillance and survey data. The population aged 15–44 years is divided into mutually exclusive exposure groups. In each group g, we simultaneously estimate the proportion of the total population belonging to the group (ρ≫), the proportion of individuals infected with HIV (π≫) and the proportion of HIV positive individuals who are diagnosed (δ≫). The total number of HIV infections, both diagnosed and undiagnosed, is then estimated as a function of the parameters ρ≫, π≫ and δ≫. Model fit is assessed by examining the posterior mean deviance. Identification of the data items to which the model exhibits a lack of fit leads to the detection of conflicting evidence, one example of which is a conflict between census data and survey data over the size of the female Sub‐Saharan African born population. This conflict arises from a naive interpretation of the representativeness of the survey data and is resolved by using two approaches: exclusion of data and expansion of the model to accommodate the bias.

Suggested Citation

  • A. M. Presanis & D. De Angelis & D. J. Spiegelhalter & S. Seaman & A. Goubar & A. E. Ades, 2008. "Conflicting evidence in a Bayesian synthesis of surveillance data to estimate human immunodeficiency virus prevalence," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 915-937, October.
    • Handle: RePEc:bla:jorssa:v:171:y:2008:i:4:p:915-937
    DOI: 10.1111/j.1467-985X.2008.00543.x
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    References listed on IDEAS

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    1. Sander Greenland, 2005. "Multiple‐bias modelling for analysis of observational data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 267-306, March.
    2. A. Goubar & A. E. Ades & D. De Angelis & C. A. McGarrigle & C. H. Mercer & P. A. Tookey & K. Fenton & O. N. Gill, 2008. "Estimates of human immunodeficiency virus prevalence and proportion diagnosed based on Bayesian multiparameter synthesis of surveillance data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 541-580, June.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Johan Giesecke & Anne Johnson & Anne Hawkins & Ahilya Noone & Angus Nicoll & Jane Wadsworth & Kaye Wellings & Julia Field, 1994. "An Estimate of the Prevalence of Human Immunodeficiency Virus Infection in England and Wales by Using a Direct Method," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(1), pages 89-103, January.
    5. A. E. Ades & A. J. Sutton, 2006. "Multiparameter evidence synthesis in epidemiology and medical decision‐making: current approaches," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 5-35, January.
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    1. S. Dias & N. J. Welton & V. C. C. Marinho & G. Salanti & J. P. T. Higgins & A. E. Ades, 2010. "Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 613-629, July.
    2. Sofia Dias & Nicky J. Welton & Alex J. Sutton & Deborah M. Caldwell & Guobing Lu & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 4," Medical Decision Making, , vol. 33(5), pages 641-656, July.

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