Journal
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY
Volume 180, Issue 1, Pages 247-261Publisher
OXFORD UNIV PRESS
DOI: 10.1111/rssa.12182
Keywords
Fetal growth; Personalized medicine; Prediction; Recursive partitioning; Shared random-effects models
Funding
- Intramural Research Program of the National Institutes of Health, NICHD
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available