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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Bandit solutions provide unified ethical models for randomized clinical trials and comparative effectiveness research
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Bandit solutions provide unified ethical models for randomized clinical trials and comparative effectiveness research

机译：Bandit解决方案为随机临床试验和比较有效性研究提供统一的道德模型

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摘要

As electronic medical records enable increasingly ambitious studies of treatment outcomes, ethical issues previously important only to limited clinical trials become relevant to unlimited whole populations. For randomized clinical trials, adaptive assignment strategies are known to expose substantially fewer patients to avoidable treatment failures than strategies with fixed assignments (e.g., equal sample sizes). An idealized adaptive case-the two-armed Bernoulli bandit problem-can be exactly optimized for a variety of ethically motivated cost functions that embody principles of duty-to-patient, but the solutions have been thought computationally infeasible when the numbers of patients in the study (the "horizon") is large. We report numerical experiments that yield a heuristic approximation that applies even to very large horizons, and we propose a near-optimal strategy that remains valid even when the horizon is unknown or unbounded, thus applicable to comparative effectiveness studies on large populations or to standard-of-care recommendations. For the case in which the economic cost of treatment is a parameter, we give a heuristic, near-optimal strategy for determining the superior treatment (whether more or less costly) while minimizing resources wasted on any inferior, more expensive, treatment. Key features of our heuristics can be generalized to more complicated protocols.
机译：由于电子病历使人们对治疗结果的研究越来越雄心勃勃，以前只对有限的临床试验重要的伦理问题就与无限的整个人群息息相关。对于随机临床试验，与具有固定分配（例如，相等的样本量）的策略相比，已知自适应分配策略可使可避免的治疗失败的患者少得多。理想的适应性案例-双臂伯努利匪徒问题-可以针对体现患者责任原则的各种出于道德动机的成本函数进行精确优化，但是当解决方案中的患者人数众多时，该解决方案在计算上是不可行的研究（“水平”）很大。我们报告了数值实验，这些实验得出的启发式近似值甚至适用于非常大的视野，并且我们提出了一种接近最优的策略，即使视野未知或不受限制，该策略仍然有效，因此适用于对大型人群或标准人群进行比较效果研究护理建议。对于以治疗的经济成本为参数的情况，我们给出了一种启发式，接近最优的策略来确定优质的治疗方法（无论成本高低），同时最大程度地减少浪费在劣质，昂贵的治疗上的资源。我们的启发式方法的关键特征可以推广到更复杂的协议。

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