Abstract
Objectives: With accurate estimates of expected safety results, clinical trials could be designed to avoid terminations and limit exposing participants to unnecessary risks. We evaluated methods for predicting serious adverse event (SAE) results in clinical trials using information only from their registrations prior to the trial. Material and Methods: We analysed 22,107 two-arm parallel interventional clinical trials from this http URL with structured summary results. Two prediction models were developed: a classifier predicting will experimental arm have higher SAE rates (area under the receiver operating characteristic curve; AUC) than control arm, and a regression model to predict the proportion of SAEs in control arms (root mean squared error; RMSE). A transfer learning approach using pretrained language models (e.g., ClinicalT5, BioBERT) was used for feature extraction, combined with downstream model for prediction. To maintain semantic representation in long trial texts exceeding localised language model input limits, a sliding window method was developed for embedding extraction. Results: The best model (ClinicalT5+Transformer+MLP) had 77.6% AUC predicting which trial arm has a higher proportion of patients with SAEs. When predicting proportion of participants experiencing SAE in the control arm, the same model achieved RMSE of 18.6%. The sliding window approach consistently outperformed methods without it. Across 12 classifiers, the average absolute AUC increase was 2.00%; across 12 regressors, the average absolute RMSE reduction was 1.58%. Discussion: Summary results data available at this http URL remains underutilised. The potential to estimate results of trials before they start is an opportunity to improve trial design and flag discrepancies between expected and reported safety results.
Abstract (translated)
目标：通过准确估算预期的安全结果，临床试验可以被设计得更加完善，避免终止，并限制参与者暴露于不必要的风险中。我们评估了使用临床试验注册信息（在试验开始之前）来预测严重不良事件（SAE）结果的方法。 材料与方法：我们分析了来自这个网址的22,107项两臂平行干预性临床试验的数据，这些数据包含结构化的总结结果。开发了两个预测模型：一个分类器用于预测实验组是否会有比对照组更高的严重不良事件率（受试者操作特性曲线下的面积AUC），以及一个回归模型来预测对照组中严重不良事件的比例（均方根误差RMSE）。采用预训练语言模型（如ClinicalT5、BioBERT）的迁移学习方法用于特征提取，结合下游模型进行预测。为了保持长篇试验文本中的语义表示，并超过本地化语言模型输入限制，开发了一种滑动窗口方法来进行嵌入抽取。 结果：最佳模型（ClinicalT5+Transformer+MLP）在预测哪一组有更高的严重不良事件患者比例时达到了77.6%的AUC。当预测对照组中经历严重不良事件的比例时，同一模型实现了18.6%的RMSE。滑动窗口方法在所有情况下都优于不使用该方法的方法。在12个分类器中，平均绝对AUC增加为2.00%；在12个回归器中，平均绝对RMSE减少了1.58%。 讨论：在这个网址上可用的总结结果数据仍然未得到充分利用。能够预测试验开始前的结果是一个改善试验设计的机会，并且可以标记预期与报告的安全性结果之间的差异。
URL
https://arxiv.org/abs/2507.22919
