期刊
BIOSTATISTICS
卷 15, 期 2, 页码 207-221出版社
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxt043
关键词
Big data; Cox proportional hazards; Regularized regression; Survival analysis
资金
- National Institute for General Medical Sciences of the National Institutes of Health [R01GM087600]
- National Science Foundation [IIS-1251151]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1251151] Funding Source: National Science Foundation
Survival analysis endures as an old, yet active research field with applications that spread across many domains. Continuing improvements in data acquisition techniques pose constant challenges in applying existing survival analysis methods to these emerging data sets. In this paper, we present tools for fitting regularized Cox survival analysis models on high-dimensional, massive sample-size (HDMSS) data using a variant of the cyclic coordinate descent optimization technique tailored for the sparsity that HDMSS data often present. Experiments on two real data examples demonstrate that efficient analyses of HDMSS data using these tools result in improved predictive performance and calibration.
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