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A Bayesian decision theoretic model of sequential experimentation with delayed response
Author(s) -
Chick Stephen,
Forster Martin,
Pertile Paolo
Publication year - 2017
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12225
Subject(s) - sequential analysis , optimal stopping , bayesian probability , sample size determination , computer science , sampling (signal processing) , sample (material) , sequential sampling , function (biology) , sequential estimation , point estimation , decision theory , statistics , artificial intelligence , algorithm , mathematics , chemistry , filter (signal processing) , chromatography , evolutionary biology , computer vision , biology , spatial distribution
Summary 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.