Europe PMC

This website requires cookies, and the limited processing of your personal data in order to function. By using the site you are agreeing to this as outlined in our privacy notice and cookie policy.

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

Author information

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.
      Authors
    • Foster JC 1
    • Liu D 1
    • Albert PS 1
    • Liu A 1
    (4 authors)

ORCIDs linked to this article

Journal of the Royal Statistical Society. Series A, (Statistics in Society), 05 Feb 2016, 180(1):247-261
https://doi.org/10.1111/rssa.12182 PMID: 28239239 PMCID: PMC5321661

Free full text in Europe PMC

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.

Free full text 

loading

Full text links 

Read article at publisher's site: https://doi.org/10.1111/rssa.12182

Read article for free, from open access legal sources, via Unpaywall: https://europepmc.org/articles/pmc5321661?pdf=renderUnpaywall

Citations & impact 

Impact metrics

5
Citations
Jump to Citations

Citations of article over time

Article citations

Similar Articles 

To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation.

Funding 

Funders who supported this work.

Intramural NIH HHS (1)

NICHD

    National Institutes of Health