Statistics and Its Interface

Volume 4 (2011)

Number 1

Bayesian decision analysis for choosing between diagnostic/prognostic prediction procedures

Pages: 27 – 36



John Kornak (Department of Epidemiology and Biostatistics, University of California at San Francisco)

Ying Lu (Department of Health Research and Policy, Stanford University, Stanford, Calif., U.S.A.)


New diagnostic procedures and prognostic markers are contnually being developed for a wide range of medical complaints. Medical institutions are therefore regularly faced with the decision as to whether to replace an existing procedure with a new one. The decision to adopt a new method is primarily based on diagnostic/predictive accuracy and cost-effectiveness, but this trade-off is not usually considered in a formal decision-theoretic way. The decision process for diagnostic procedures is complicated by the fact that diagnostic decisions are typically based on thresholding one or more continuous variables. Therefore, a formal decision process should account for uncertainty in the optimal threshold value for each diagnostic procedure. We here propose a Bayesian decision approach based on maximizing expected utility (incorporating accuracy and costs) with respect to diagnostic procedure and threshold level simultaneously. The Bayesian decision approach is illustrated via an application comparing the utility of different bone mineral density (BMD) measurements for determining the need for preventative treatment of osteoporotic hip fracture in elderly patients.


Bayesian decision analysis, decision theory, diagnostic methods

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