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

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

PLOS Computational Biology, 2017, vol. 13, issue 12, 1-21

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.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005887 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 05887&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005887

DOI: 10.1371/journal.pcbi.1005887

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().