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A Bayesian approach to sample size determination for studies designed to evaluate continuous medical tests

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  • Cheng, Dunlei
  • Branscum, Adam J.
  • Stamey, James D.

Abstract

We develop a Bayesian approach to sample size and power calculations for cross-sectional studies that are designed to evaluate and compare continuous medical tests. For studies that involve one test or two conditionally independent or dependent tests, we present methods that are applicable when the true disease status of sampled individuals will be available and when it will not. Within a hypothesis testing framework, we consider the goal of demonstrating that a medical test has area under the receiver operating characteristic (ROC) curve that exceeds a minimum acceptable level or another relevant threshold, and the goals of establishing the superiority or equivalence of one test relative to another. A Bayesian average power criterion is used to determine a sample size that will yield high posterior probability, on average, of a future study correctly deciding in favor of these goals. The impacts on Bayesian average power of prior distributions, the proportion of diseased subjects in the study, and correlation among tests are investigated through simulation. The computational algorithm we develop involves simulating multiple data sets that are fit with Bayesian models using Gibbs sampling, and is executed by using WinBUGS in tandem with R.

Suggested Citation

  • Cheng, Dunlei & Branscum, Adam J. & Stamey, James D., 2010. "A Bayesian approach to sample size determination for studies designed to evaluate continuous medical tests," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 298-307, February.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:2:p:298-307
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    References listed on IDEAS

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    1. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    2. Usotskaya, N. & Ryabko, B., 2009. "Application of information-theoretic tests for the analysis of DNA sequences based on Markov chain models," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1861-1872, March.
    3. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
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    Cited by:

    1. Rauch, G. & Kieser, M., 2013. "An expected power approach for the assessment of composite endpoints and their components," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 111-122.

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