人工智能代理用于前后数据治疗效果估计
Artificial intelligence surrogates for treatment effect estimation with before-and-after data
- University of California, Berkeley（加州大学伯克利分校）
- University of California, San Francisco（加州大学旧金山分校）
- Williams College（威廉姆斯学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对临床结局测量昂贵或随访长的问题，提出利用AI预测作为替代指标，基于前后配对测量估计治疗效应，并通过预测增强推断校正偏差，实验验证有效。
AI中文摘要：
当临床重要结局的测量成本高昂或需要长期随访时，估计医疗治疗因果效应变得困难。短期或廉价的替代结局提供了一种潜在替代方案，但替代生物标志物可能不可用或难以识别。人工智能（AI）的进步使得从廉价的高维测量中越来越准确地预测临床结局成为可能，这为利用AI预测本身作为替代指标创造了机会。为此，我们开发了一个框架，用于从每个接受治疗个体在治疗前后获得的配对测量中估计治疗效果。将预训练的AI模型应用于前后测量，我们的估计器比较由此产生的结局预测。我们描述了在何种技术假设下，这种个体内对比能够识别接受治疗者的平均治疗效果，即使对于接受治疗的个体从未观察到临床结局。当这些假设无法成立时，我们使用预测增强推断，利用少量观察到的临床结局校正偏差并获得有效的推断。合成和真实世界的心脏肿瘤学实验证明了该方法的有效性和准确性。
英文摘要：
Estimating the causal effects of medical treatments is difficult when clinically important outcomes are costly to measure or require long follow-up. Short-term or inexpensive surrogate outcomes offer a potential alternative, but surrogate biomarkers may be unavailable or difficult to identify. Advances in artificial intelligence (AI) have enabled increasingly accurate prediction of clinical outcomes from inexpensive, high-dimensional measurements, which creates an opportunity to use AI predictions themselves as surrogates. To this end, we develop a framework for estimating treatment effects from paired measurements obtained before and after treatment for each treated individual. A pretrained AI model is applied to the before and after measurements, and our estimator compares the resulting outcome predictions. We characterize the technical assumptions under which this within-person contrast identifies the average treatment effect on the treated, even when clinical outcomes are never observed for treated individuals. When these assumptions cannot be justified, we use prediction-powered inference to correct bias using a small number of observed clinical outcomes and obtain valid inference. Synthetic and real-world cardio-oncology experiments demonstrate the validity and accuracy of the approach.
