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Foster JC, Liu D, Albert PS, et al. Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data using tree-based approaches: applications to fetal growth. J R Stat Soc Ser A Stat Soc. 2016;180(1):247-261doi: 10.1111/rssa.12182.
Foster, J. C., Liu, D., Albert, P. S., & Liu, A. (2017). Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data using tree-based approaches: applications to fetal growth. Journal of the Royal Statistical Society. Series A, (Statistics in Society), 180(1), 247-261. https://doi.org/10.1111/rssa.12182
Foster, Jared C, et al. "Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data using tree-based approaches: applications to fetal growth." Journal of the Royal Statistical Society. Series A, (Statistics in Society) vol. 180,1 (2017): 247-261. doi: https://doi.org/10.1111/rssa.12182
Foster JC, Liu D, Albert PS, Liu A. Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data using tree-based approaches: applications to fetal growth. J R Stat Soc Ser A Stat Soc. 2017 Jan;180(1):247-261. doi: 10.1111/rssa.12182. Epub 2016 Feb 05. PMID: 28239239; PMCID: PMC5321661.
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J R Stat Soc Ser A Stat Soc. 2017 Jan;180(1):247-261. doi: 10.1111/rssa.12182. Epub 2016 Feb 05.

Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data using tree-based approaches: applications to fetal growth.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)

Jared C Foster, Danping Liu, Paul S Albert, Aiyi Liu

Affiliations

  1. Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA.

PMID: 28239239 PMCID: PMC5321661 DOI: 10.1111/rssa.12182

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.

Keywords: Fetal Growth; Personalized Medicine; Prediction; Recursive Partitioning; Shared Random Effects Models

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