Abstract
We propose a Bayesian decision theoretic model of a fully sequential experiment in which the real-valued primary end point is observed with delay. The goal is to identify the sequential experiment which maximizes the expected benefits of technology adoption decisions, minus sampling costs. The solution yields a unified policy defining the optimal ‘do not experiment’–‘fixed sample size experiment’–‘sequential experiment’ regions and optimal stopping boundaries for sequential sampling, as a function of the prior mean benefit and the size of the delay. We apply the model to the field of medical statistics, using data from published clinical trials.
| Original language | English |
|---|---|
| Pages (from-to) | 1439-1462 |
| Number of pages | 24 |
| Journal | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY |
| Volume | 79 |
| Issue number | 5 |
| Early online date | 9 Jan 2017 |
| DOIs | |
| Publication status | Published - 30 Oct 2017 |
Bibliographical note
© Wiley, 2017. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for detailsKeywords
- Bayesian inference
- Clinical trials
- Delayed observations
- Health economics
- Sequential experimentation
