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Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

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  • Julio Montes-Torres
  • José Luis Subirats
  • Nuria Ribelles
  • Daniel Urda
  • Leonardo Franco
  • Emilio Alba
  • José Manuel Jerez

Abstract

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0161135
    DOI: 10.1371/journal.pone.0161135
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    References listed on IDEAS

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    1. Jae-Seong Yang & Hyun-Jun Nam & Mihwa Seo & Seong Kyu Han & Yonghwan Choi & Hong Gil Nam & Seung-Jae Lee & Sanguk Kim, 2011. "OASIS: Online Application for the Survival Analysis of Lifespan Assays Performed in Aging Research," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-11, August.
    2. Crowther, Michael J. & Lambert, Paul C., 2013. "stgenreg: A Stata Package for General Parametric Survival Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i12).
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    Cited by:

    1. 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.
    2. Liu, Meijun & Hu, Xiao & Wang, Yuandi & Shi, Dongbo, 2018. "Survive or perish: Investigating the life cycle of academic journals from 1950 to 2013 using survival analysis methods," Journal of Informetrics, Elsevier, vol. 12(1), pages 344-364.

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