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Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo‐observation approach
Author(s) -
Zhao Lili,
Murray Susan,
Mariani Laura H.,
Ju Wenjun
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8687
Subject(s) - computer science , biomarker , curse of dimensionality , data set , biomarker discovery , dimension (graph theory) , sample size determination , data mining , data science , machine learning , artificial intelligence , statistics , proteomics , mathematics , biochemistry , chemistry , pure mathematics , gene
Longitudinal biomarker data are often collected in studies, providing important information regarding the probability of an outcome of interest occurring at a future time. With many new and evolving technologies for biomarker discovery, the number of biomarker measurements available for analysis of disease progression has increased dramatically. A large amount of data provides a more complete picture of a patient's disease progression, potentially allowing us to make more accurate and reliable predictions, but the magnitude of available data introduces challenges to most statistical analysts. Existing approaches suffer immensely from the curse of dimensionality. In this article, we propose methods for making dynamic risk predictions using repeatedly measured biomarkers of a large dimension, including cases when the number of biomarkers is close to the sample size. The proposed methods are computationally simple, yet sufficiently flexible to capture complex relationships between longitudinal biomarkers and potentially censored events times. The proposed approaches are evaluated by extensive simulation studies and are further illustrated by an application to a data set from the Nephrotic Syndrome Study Network.