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Complete hazard ranking to analyze right-censored data: An ALS survival study

Author

Listed:
  • Zhengnan Huang
  • Hongjiu Zhang
  • Jonathan Boss
  • Stephen A Goutman
  • Bhramar Mukherjee
  • Ivo D Dinov
  • Yuanfang Guan
  • for the Pooled Resource Open-Access ALS Clinical Trials Consortium

Abstract

Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.Author summary: We present a novel rank-based algorithm that outputs the survival likelihood of the ALS patients from the survival data. This novel method enabled us to adopt commonplace machine learning base-learners in survival analysis of ALS and provided insight into the disease.

Suggested Citation

  • Zhengnan Huang & Hongjiu Zhang & Jonathan Boss & Stephen A Goutman & Bhramar Mukherjee & Ivo D Dinov & Yuanfang Guan & for the Pooled Resource Open-Access ALS Clinical Trials Consortium, 2017. "Complete hazard ranking to analyze right-censored data: An ALS survival study," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-21, December.
    • Handle: RePEc:plo:pcbi00:1005887
    DOI: 10.1371/journal.pcbi.1005887
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    References listed on IDEAS

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    1. Thomas H. Scheike & Torben Martinussen, 2004. "On Estimation and Tests of Time‐Varying Effects in the Proportional Hazards Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(1), pages 51-62, March.
    2. Malloy, Elizabeth J. & Spiegelman, Donna & Eisen, Ellen A., 2009. "Comparing measures of model selection for penalized splines in Cox models," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2605-2616, May.
    3. Branko Miladinovic & Ambuj Kumar & Rahul Mhaskar & Sehwan Kim & Ronald Schonwetter & Benjamin Djulbegovic, 2012. "A Flexible Alternative to the Cox Proportional Hazards Model for Assessing the Prognostic Accuracy of Hospice Patient Survival," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-8, October.
    4. Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
    5. Mogensen, Ulla B. & Ishwaran, Hemant & Gerds, Thomas A., 2012. "Evaluating Random Forests for Survival Analysis Using Prediction Error Curves," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i11).
    6. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    7. Julio Montes-Torres & José Luis Subirats & Nuria Ribelles & Daniel Urda & Leonardo Franco & Emilio Alba & José Manuel Jerez, 2016. "Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.
    8. Hemant Ishwaran & Eugene H. Blackstone & Claire E. Pothier & Michael S. Lauer, 2004. "Relative Risk Forests for Exercise Heart Rate Recovery as a Predictor of Mortality," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 591-600, January.
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