Work

Identifying Subgroups of Enhanced Predictive Accuracy from Longitudinal Biomarker Data by Using Tree-Based Approaches: Applications to Fetal Growth

Year: 2016

Type: article

Abstract: 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.

Source: Journal of the Royal Statistical Society Series A (Statistics in Society)

DOI: 10.1111/rssa.12182

Authors: Jared C. Foster, Danping Liu, Paul S. Albert, Aiyi Liu

Institution: Eunice Kennedy Shriver National Institute of Child Health and Human Development

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,456