Identifying Subgroups of Enhanced Predictive Accuracy from Longitudinal Biomarker Data by Using Tree-Based Approaches: Applications to Fetal Growth
提出一种树方法，利用纵向超声测量数据识别预测胎儿大小异常（过大或过小）更准确或更不准确的孕妇亚组，并控制假发现风险。
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.