A Bayesian Decision Theoretic Model of Sequential Experimentation with Delayed Response
提出了一个贝叶斯决策理论模型，用于在实验终点数据延迟观测的情况下，设计最优序贯实验，以最大化技术采纳的期望收益并控制采样成本，并利用临床试验数据进行了应用。
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.