Abstract
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 language | English |
|---|---|
| Pages (from-to) | 419-438 |
| Number of pages | 20 |
| Journal | Journal of the Royal Statistical Society: Series A (Statistics in Society) |
| Volume | 177 |
| Issue number | 2 |
| Early online date | 9 Aug 2013 |
| DOIs | |
| Publication status | Published - Feb 2014 |
Keywords
- Bayesian
- Dynamic programming
- Economic evaluation
- Sequential sampling