Journal article
TIME-VARYING HAZARDS MODEL FOR INCORPORATING IRREGULARLY MEASURED HIGH-DIMENSIONAL BIOMARKERS
Statistica Sinica, Vol.30(3), pp.1605-1632
07/01/2020
DOI: 10.5705/ss.202017.0375
PMCID: PMC7497773
PMID: 32952367
Abstract
Clinical studies with time-to-event outcomes often collect measurements of a large number of time-varying covariates over time (e.g., clinical assessments or neuroimaging biomarkers) in order to build a time-sensitive prognostic model. However, resource-intensive or invasive (e.g., lumbar puncture) data-collection processes mean that biomarkers may be measured infrequently and, thus, not be available at every observed event time point. Therefore, leveraging all available time-varying biomarkers is important to improving our models event occurrence. We propose a kernel smoothing-based approach that borrows information across subjects to remedy the problem of infrequent and unbalanced biomarker measurements under a time-varying hazards model. A penalized pseudo-likelihood function is proposed for estimation, and an efficient augmented penalization minimization algorithm related to the alternating direction method of multipliers is adopted for computation. Given several regularity conditions, used to control the approximation bias and stochastic variability, we show that even in the presence of ultrahigh dimensionality, the proposed method selects important biomarkers with high probability. We use simulation studies to show that our method outperforms existing methods in terms of estimation and selection performance. Finally, we apply the proposed method to real data to model time-to-disease conversion using longitudinal, whole-brain structural magnetic resonance imaging biomarkers. The results show substantial improvement in performance over that of current standards, including using baseline measures only.
Details
- Title: Subtitle
- TIME-VARYING HAZARDS MODEL FOR INCORPORATING IRREGULARLY MEASURED HIGH-DIMENSIONAL BIOMARKERS
- Creators
- Xiang Li - Columbia UniversityQuefeng Li - University of North Carolina at Chapel HillDonglin Zeng - University of North Carolina at Chapel HillKaren Marder - Columbia UniversityJane Paulsen - University of IowaYuanjia Wang - Columbia University
- Resource Type
- Journal article
- Publication Details
- Statistica Sinica, Vol.30(3), pp.1605-1632
- DOI
- 10.5705/ss.202017.0375
- PMID
- 32952367
- PMCID
- PMC7497773
- NLM abbreviation
- Stat Sin
- ISSN
- 1017-0405
- eISSN
- 1996-8507
- Publisher
- Statistica Sinica
- Number of pages
- 28
- Grant note
- NS073671; GM124104; MH117458 / U.S. NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Language
- English
- Date published
- 07/01/2020
- Academic Unit
- Psychiatry; Psychological and Brain Sciences
- Record Identifier
- 9984383895202771
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