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Taking Costs and Diagnostic Test Accuracy into Account When Designing Prevalence Studies: An Application to Childhood Tuberculosis Prevalence

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  • Zhuoyu Wang
  • Nandini Dendukuri
  • Madhukar Pai
  • Lawrence Joseph

Abstract

Background. When planning a study to estimate disease prevalence to a pre-specified precision, it is of interest to minimize total testing cost. This is particularly challenging in the absence of a perfect reference test for the disease because different combinations of imperfect tests need to be considered. We illustrate the problem and a solution by designing a study to estimate the prevalence of childhood tuberculosis in a hospital setting. Methods. All possible combinations of 3 commonly used tuberculosis tests, including chest X-ray, tuberculin skin test, and a sputum-based test, either culture or Xpert, are considered. For each of the 11 possible test combinations, 3 Bayesian sample size criteria, including average coverage criterion, average length criterion and modified worst outcome criterion, are used to determine the required sample size and total testing cost, taking into consideration prior knowledge about the accuracy of the tests. Results. In some cases, the required sample sizes and total testing costs were both reduced when more tests were used, whereas, in other examples, lower costs are achieved with fewer tests. Conclusion. Total testing cost should be formally considered when designing a prevalence study.

Suggested Citation

  • Zhuoyu Wang & Nandini Dendukuri & Madhukar Pai & Lawrence Joseph, 2017. "Taking Costs and Diagnostic Test Accuracy into Account When Designing Prevalence Studies: An Application to Childhood Tuberculosis Prevalence," Medical Decision Making, , vol. 37(8), pages 922-929, November.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:8:p:922-929
    DOI: 10.1177/0272989X17713456
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    References listed on IDEAS

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    1. Lucy Cunnama & Edina Sinanovic & Lebogang Ramma & Nicola Foster & Leigh Berrie & Wendy Stevens & Sebaka Molapo & Puleng Marokane & Kerrigan McCarthy & Gavin Churchyard & Anna Vassall, 2016. "Using Top‐down and Bottom‐up Costing Approaches in LMICs: The Case for Using Both to Assess the Incremental Costs of New Technologies at Scale," Health Economics, John Wiley & Sons, Ltd., vol. 25(S1), pages 53-66, February.
    2. Nandini Dendukuri & Elham Rahme & Patrick Bélisle & Lawrence Joseph, 2004. "Bayesian Sample Size Determination for Prevalence and Diagnostic Test Studies in the Absence of a Gold Standard Test," Biometrics, The International Biometric Society, vol. 60(2), pages 388-397, June.
    3. E. Rahme & L. Joseph & T. W. Gyorkos, 2000. "Bayesian sample size determination for estimating binomial parameters from data subject to misclassification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(1), pages 119-128.
    4. Lucy Cunnama & Edina Sinanovic & Lebogang Ramma & Nicola Foster & Leigh Berrie & Wendy Stevens & Sebaka Molapo & Puleng Marokane & Kerrigan McCarthy & Gavin Churchyard & Anna Vassall, 2016. "Using Top‐down and Bottom‐up Costing Approaches in LMICs: The Case for Using Both to Assess the Incremental Costs of New Technologies at Scale," Health Economics, John Wiley & Sons, Ltd., vol. 25, pages 53-66, February.
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