Abstract
There is an increasing need to construct a risk-prediction scoring system for survival data and identify important risk factors (e.g., biomarkers) for patient screening and treatment recommendation. However, most existing methodologies either rely on strong model assumptions (e.g., proportional hazards) or only handle binary outcomes. In this article, we propose a flexible method that simultaneously selects important risk factors and identifies the optimal linear combination of risk factors by maximizing a pseudo-likelihood function based on the time-dependent area under the receiver operating characteristic curve. Our method is particularly useful for risk evaluation and recommendation of optimal subsequent treatments. We show that the proposed method has desirable theoretical properties, including asymptotic normality and the oracle property after variable selection. Numerical performance is evaluated on several simulation data sets and an application to hepatocellular carcinoma data.
Original language | English (US) |
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Pages (from-to) | 239-248 |
Number of pages | 10 |
Journal | Biometrics |
Volume | 74 |
Issue number | 1 |
DOIs | |
State | Published - Mar 2018 |
Keywords
- Biomarker
- Liver cancer
- Risk prediction
- Scoring system
- Time-dependent AUC
- Variable selection
ASJC Scopus subject areas
- Statistics and Probability
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics
MD Anderson CCSG core facilities
- Biostatistics Resource Group