Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer’s disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer’s patients.

目标试验模拟是使用真实世界数据模拟目标随机试验的过程，其中对无偏治疗效果估计的有效混杂控制仍然是一个主要挑战。尽管已经针对这一挑战提出了各种方法，但仍然缺乏系统评价。在这里，我们模拟了来自两个大规模真实世界数据仓库的数千种药物的试验，涵盖了超过 1.7 亿患者的 10 多年临床记录，旨在确定已批准用于治疗阿尔茨海默病的药物的新适应症。我们在治疗加权的逆概率框架下评估了不同的倾向评分模型，并提出了一种改进基线协变量平衡的模型选择策略。我们还发现，基于深度学习的倾向得分模型在协变量平衡方面不一定优于基于 logistic 回归的方法。最后，我们重点介绍了五种排名靠前的药物（泮托拉唑、加巴喷丁、阿托伐他汀、氟替卡松和奥美拉唑），这些药物最初用于其他适应症，对阿尔茨海默病患者有潜在益处。