IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1005887.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005887
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005887&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1005887?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lore Zumeta-Olaskoaga & Maximilian Weigert & Jon Larruskain & Eder Bikandi & Igor Setuain & Josean Lekue & Helmut Küchenhoff & Dae-Jin Lee, 2023. "Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 101-126, March.
    2. Julia Gilhodes & Florence Dalenc & Jocelyn Gal & Christophe Zemmour & Eve Leconte & Jean Marie Boher & Thomas Filleron, 2020. "Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings," Post-Print hal-02934793, HAL.
    3. Goedhart, Jeroen M. & Klausch, Thomas & van de Wiel, Mark A., 2023. "Estimation of predictive performance in high-dimensional data settings using learning curves," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    4. Sill, Martin & Hielscher, Thomas & Becker, Natalia & Zucknick, Manuela, 2014. "c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i05).
    5. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    6. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.
    7. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    8. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    9. Masakazu Higuchi & Mitsuteru Nakamura & Shuji Shinohara & Yasuhiro Omiya & Takeshi Takano & Daisuke Mizuguchi & Noriaki Sonota & Hiroyuki Toda & Taku Saito & Mirai So & Eiji Takayama & Hiroo Terashi &, 2022. "Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach," IJERPH, MDPI, vol. 19(18), pages 1-13, September.
    10. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    11. Vincent, Martin & Hansen, Niels Richard, 2014. "Sparse group lasso and high dimensional multinomial classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 771-786.
    12. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    13. Perrot-Dockès Marie & Lévy-Leduc Céline & Chiquet Julien & Sansonnet Laure & Brégère Margaux & Étienne Marie-Pierre & Robin Stéphane & Genta-Jouve Grégory, 2018. "A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(5), pages 1-14, October.
    14. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    15. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    16. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
    17. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    18. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    19. Xiangwei Li & Thomas Delerue & Ben Schöttker & Bernd Holleczek & Eva Grill & Annette Peters & Melanie Waldenberger & Barbara Thorand & Hermann Brenner, 2022. "Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    20. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1005887. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.