论文标题
因果效应估计和移动健康中的最佳剂量建议
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health
论文作者
论文摘要
在本文中，我们提出了新型的结构嵌套模型，以根据移动健康数据估算连续治疗的因果关系。为了找到优化患者预期的短期结局的治疗方案，我们将加权滞后优势定义为价值函数。然后将最佳处理方案定义为最大化值函数的治疗方案。我们的方法对数据生成过程施加了最少的假设。为估计参数提供了统计推断。模拟研究和对俄亥俄州1型糖尿病数据集的应用显示，我们的方法可以通过移动健康数据为剂量建议提供有意义的见解。
In this article, we propose novel structural nested models to estimate causal effects of continuous treatments based on mobile health data. To find the treatment regime that optimizes the expected short-term outcomes for patients, we define a weighted lag-K advantage as the value function. The optimal treatment regime is then defined to be the one that maximizes the value function. Our method imposes minimal assumptions on the data generating process. Statistical inference is provided for the estimated parameters. Simulation studies and an application to the Ohio type 1 diabetes dataset show that our method could provide meaningful insights for dose suggestions with mobile health data.
