Longitudinal monitoring of biomarkers is often helpful for predicting disease or a poor clinical outcome. In this paper, We consider the prediction of both large and small-for-gestational-age births using longitudinal ultrasound measurements, and attempt to identify subgroups of women for whom prediction is more (or less) accurate, should they exist. We propose a tree-based approach to identifying such subgroups, and a pruning algorithm which explicitly incorporates a desired type-I error rate, allowing us to control the risk of false discovery of subgroups. The proposed methods are applied to data from the Scandinavian Fetal Growth Study, and are evaluated via simulations.

中文翻译：

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使用基于树的方法从纵向生物标记数据中识别出提高预测准确性的亚组：在胎儿生长中的应用。

纵向监测生物标志物通常有助于预测疾病或不良的临床结果。在本文中，我们考虑使用纵向超声测量对大和小胎龄儿的胎儿进行预测，并尝试确定预测准确度更高（或更低）的女性亚组。我们提出了一种基于树的方法来识别此类子组，并提出了一种修剪算法，该算法明确包含了所需的I型错误率，从而使我们能够控制错误发现子组的风险。拟议的方法应用于来自斯堪的纳维亚胎儿生长研究的数据，并通过模拟进行评估。