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Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data by using tree‐based approaches: applications to fetal growth
Author(s) -
Foster Jared C.,
Liu Danping,
Albert Paul S.,
Liu Aiyi
Publication year - 2017
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12182
Subject(s) - pruning , tree (set theory) , longitudinal data , computer science , statistics , data mining , machine learning , econometrics , artificial intelligence , mathematics , biology , mathematical analysis , agronomy
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.