Participation incentives a well-known issue inhibiting randomized clinical trials (RCTs). We frame this issue as a non-standard exploration-exploitation tradeoff: an RCT would like to explore as uniformly as possible, whereas each patient prefers "exploitation", i.e., treatments that seem best. We incentivize participation by leveraging information asymmetry between the trial and the patients. We measure statistical performance via worst-case estimation error under adversarially generated outcomes, a standard objective for RCTs. We obtain a near-optimal solution in terms of this objective: an incentive-compatible mechanism with a particular guarantee, and a nearly matching impossibility result for any incentive-compatible mechanism. We consider three model variants: homogeneous patients (of the same "type" comprising preferences and medical histories), heterogeneous agents, and an extension with estimated type frequencies.

翻译：参与激励是制约随机对照试验（RCTs）的公认难题。我们将该问题重构为非标准型探索-利用权衡：随机对照试验追求尽可能均匀的探索，而每位患者更偏好"利用"——即看似最优的治疗方案。我们通过利用试验方与患者之间的信息不对称性来激励参与。采用对抗性生成结果下的最坏情况估计误差作为统计性能度量标准，这是随机对照试验的标准目标。我们在此目标下获得近最优解：一个具有特定保证的激励相容机制，以及针对任意激励相容机制的几乎匹配的不可能性结论。我们考虑三种模型变体：同质患者（包含偏好与病史的相同"类型"）、异质主体，以及含估计类型频率的扩展模型。