The content delves into the challenges of recruiting patients for clinical trials, framing it as a tradeoff between exploration and exploitation. By leveraging information assymetry, the authors propose mechanisms to encourage participation while optimizing statistical performance. The study extends to heterogeneous agents, providing insights on incentivized participation strategies.
Clinical trials aim to evaluate medical treatments, with randomization crucial for unbiased results. Challenges in patient recruitment hinder large-scale trials despite treatment availability. The study introduces a mechanism design problem focusing on incentivized participation under statistical objectives and incentive constraints.
The proposed mechanism uses a two-stage design with warm-up data creating information asymmetry for participation incentives. Assumptions align with standard practices in clinical trials regarding information disclosure and policy commitment. The study extends its findings from homogeneous to heterogeneous agents, addressing different types' preferences and outcomes.
Results showcase optimal solutions for each model variant, emphasizing worst-case estimation error minimization under various adversaries. Mechanisms are designed to be stationary during the main stage, simplifying adaptation challenges faced in practice. The content highlights the significance of incentivized participation in clinical trials and connects statistical objectives with an information design framework.
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by Yingkai Li,A... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2202.06191.pdfDeeper Inquiries