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Model†free scoring system for risk prediction with application to hepatocellular carcinoma study

Author

Listed:
  • Weining Shen
  • Jing Ning
  • Ying Yuan
  • Anna S. Lok
  • Ziding Feng

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.

Suggested Citation

  • Weining Shen & Jing Ning & Ying Yuan & Anna S. Lok & Ziding Feng, 2018. "Model†free scoring system for risk prediction with application to hepatocellular carcinoma study," Biometrics, The International Biometric Society, vol. 74(1), pages 239-248, March.
  • Handle: RePEc:bla:biomet:v:74:y:2018:i:1:p:239-248
    DOI: 10.1111/biom.12750
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Patrick J. Heagerty & Yingye Zheng, 2005. "Survival Model Predictive Accuracy and ROC Curves," Biometrics, The International Biometric Society, vol. 61(1), pages 92-105, March.
    3. Zheng Yuan & Debashis Ghosh, 2008. "Combining Multiple Biomarker Models in Logistic Regression," Biometrics, The International Biometric Society, vol. 64(2), pages 431-439, June.
    4. Yingye Zheng & Tianxi Cai & Ziding Feng, 2006. "Application of the Time-Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers," Biometrics, The International Biometric Society, vol. 62(1), pages 279-287, March.
    5. Chen, Xiwei & Vexler, Albert & Markatou, Marianthi, 2015. "Empirical likelihood ratio confidence interval estimation of best linear combinations of biomarkers," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 186-198.
    6. Jones, M. C., 1990. "The performance of kernel density functions in kernel distribution function estimation," Statistics & Probability Letters, Elsevier, vol. 9(2), pages 129-132, February.
    7. Zeng, Donglin & Lin, D.Y., 2007. "Efficient Estimation for the Accelerated Failure Time Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1387-1396, December.
    8. Margaret Sullivan Pepe & Tianxi Cai & Gary Longton, 2006. "Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve," Biometrics, The International Biometric Society, vol. 62(1), pages 221-229, March.
    9. Tianxi Cai & Yingye Zheng, 2013. "Resampling Procedures for Making Inference Under Nested Case--Control Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1532-1544, December.
    10. Weining Shen & Jing Ning & Ying Yuan, 2015. "A direct method to evaluate the time-dependent predictive accuracy for biomarkers," Biometrics, The International Biometric Society, vol. 71(2), pages 439-449, June.
    11. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    12. Martin W. McIntosh & Margaret Sullivan Pepe, 2002. "Combining Several Screening Tests: Optimality of the Risk Score," Biometrics, The International Biometric Society, vol. 58(3), pages 657-664, September.
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