Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies

Paolo Pertile, Martin Forster, Davide La Torre

Research output: Contribution to journalArticlepeer-review


We present a Bayes sequential economic evaluation model for health technologies in which an investigator has flexibility over the timing of a decision to stop carrying out research and to conclude that one technology is preferred to another on cost-effectiveness grounds. We implement the model by using an evaluation of the treatment of bacterial sinusitis and derive approximations of the optimal stopping rule as a function of accumulated sample size. We compare the performance of the model with existing frequentist and Bayes sequential designs and investigate the sensitivity of the stopping rule to changes in the parameters of the model. Our results suggest that accounting for the dynamic nature of experimentation, together with its economic parameters, should lead to greater efficiency in resource allocation within healthcare systems.
Original languageEnglish
Pages (from-to)419-438
Number of pages20
JournalJournal of the Royal Statistical Society: Series A (Statistics in Society)
Issue number2
Early online date9 Aug 2013
Publication statusPublished - Feb 2014


  • Bayesian
  • Dynamic programming
  • Economic evaluation
  • Sequential sampling

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