论文标题
付费研究的限制D-最佳设计
Constrained D-optimal Design for Paid Research Study
论文作者
论文摘要
我们考虑在付费研究或临床试验中进行采样问题的限制。当合格的志愿者超过预算允许的范围时，我们建议基于最佳设计理论的D-最佳抽样策略，并开发一种约束的升力算法以找到最佳分配。与主要涉及线性模型的文献不同，我们的解决方案在相当一般的统计模型（包括通用的线性模型和多项式逻辑模型）以及更一般的约束下解决了约束采样问题。从理论上讲，我们证明采样策略的最佳性是合理的，并通过模拟研究和现实世界的示例来显示优于简单的随机抽样和按比例分层的采样策略的优势。
We consider constrained sampling problems in paid research studies or clinical trials. When qualified volunteers are more than the budget allowed, we recommend a D-optimal sampling strategy based on the optimal design theory and develop a constrained lift-one algorithm to find the optimal allocation. Unlike the literature which mainly deals with linear models, our solution solves the constrained sampling problem under fairly general statistical models, including generalized linear models and multinomial logistic models, and with more general constraints. We justify theoretically the optimality of our sampling strategy and show by simulation studies and real-world examples the advantages over simple random sampling and proportionally stratified sampling strategies.
