Shen Dennis, Agarwal Anish, Misra Vishal, Schelter Bjoern, Shah Devavrat, Shiells Helen, Wischik Claude
Department of Data Sciences and Operations, USC, Los Angeles, USA.
Department of Industrial Engineering and Operations Research, Columbia University, New York, USA.
Sci Rep. 2025 Jan 11;15(1):1671. doi: 10.1038/s41598-024-84687-4.
The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages information across patients to impute missing data associated with each patient of interest. We focus on two types of missing data: (i) unrecorded outcomes from discontinuing the assigned treatments and (ii) unobserved outcomes associated with unassigned treatments. Data imputation in the former powers and de-biases RCTs, while data imputation in the latter simulates "synthetic RCTs" to predict the outcomes for each patient under every treatment. The SNN estimator is interpretable, transparent, and causally justified under a broad class of missing data scenarios. Relative to several standard methods, we empirically find that SNN performs well for the above two applications using Phase 3 clinical trial data on patients with Alzheimer's Disease. Our findings directly suggest that SNN can tackle a current pain point within the clinical trial workflow on patient dropouts and serve as a new tool towards the development of precision medicine. Building on our insights, we discuss how SNN can further generalize to real-world applications.
本文的目的是从群体水平的随机对照试验（RCT）中推断患者个体水平的结果。在此过程中，我们使用了最近提出的合成最近邻（SNN）估计器。SNN的核心是利用患者之间的信息来填补与每个感兴趣患者相关的缺失数据。我们关注两种类型的缺失数据：（i）因停止分配的治疗而未记录的结果，以及（ii）与未分配治疗相关的未观察到的结果。前者的数据填补增强了RCT的效力并消除了偏差，而后者的数据填补则模拟“合成RCT”以预测每个患者在每种治疗下的结果。在广泛的缺失数据场景下，SNN估计器具有可解释性、透明度且在因果关系上是合理的。相对于几种标准方法，我们通过实证发现，使用阿尔茨海默病患者的3期临床试验数据，SNN在上述两种应用中表现良好。我们的研究结果直接表明，SNN可以解决临床试验工作流程中当前关于患者退出的痛点，并作为开发精准医学的新工具。基于我们的见解，我们讨论了SNN如何进一步推广到实际应用中。
