Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data by using tree‐based approaches: applications to fetal growth. (5th February 2016)

• Record Type: Journal Article
• Authors: Foster, Jared C.; Liu, Danping; Albert, Paul S.; Liu, Aiyi
• Is Part Of: Journal of the Royal Statistical Society. Volume 180:Number 1(2017:Jan.)
• Page Start: 247
• Page End: 261
• Publication Date: 2016-02-05
• Subjects: Fetal growth -- Personalized medicine -- Prediction -- Recursive partitioning -- Shared random‐effects models; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12182
• Languages: English
• ISSNs: 0964-1998

Abstract

Summary: Longitudinal monitoring of biomarkers is often helpful for predicting disease or a poor clinical outcome. We consider the prediction of both large and small for gestational age births by using longitudinal ultrasound measurements, and we 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 methods proposed are applied to data from the Scandinavian Fetal Growth Study and are evaluated via simulations.