IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v26y2020i4d10.1007_s10985-020-09504-2.html
   My bibliography  Save this article

Extensions of the absolute standardized hazard ratio and connections with measures of explained variation and variable importance

Author

Listed:
  • Michael R. Crager

    (Exact Sciences Corporation)

Abstract

The absolute standardized hazard ratio (ASHR) is a scale-invariant scalar measure of the strength of association of a vector of covariates with the risk of an event. It is derived from proportional hazards regression. The ASHR is useful for making comparisons among different sets of covariates. Extensions of the ASHR concept and practical considerations regarding its computation are discussed. These include a new method to conduct preliminary checks for collinearity among covariates, a partial ASHR to evaluate the association with event risk of some of the covariates conditioning on others, and the ASHR for interactions. To put the ASHR in context, its relationship to measures of explained variation and other measures of separation of risk is discussed. A new measure of the contribution of each covariate to the risk score variance is proposed. This measure, which is derived from the ASHR calculations, is interpretable as variable importance within the context of the multivariable model.

Suggested Citation

  • Michael R. Crager, 2020. "Extensions of the absolute standardized hazard ratio and connections with measures of explained variation and variable importance," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 872-892, October.
  • Handle: RePEc:spr:lifeda:v:26:y:2020:i:4:d:10.1007_s10985-020-09504-2
    DOI: 10.1007/s10985-020-09504-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-020-09504-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10985-020-09504-2?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Johan Bring, 1995. "Variable importance by partitioningR 2," Quality & Quantity: International Journal of Methodology, Springer, vol. 29(2), pages 173-189, May.
    2. Michael Schemper & Robin Henderson, 2000. "Predictive Accuracy and Explained Variation in Cox Regression," Biometrics, The International Biometric Society, vol. 56(1), pages 249-255, March.
    3. Michael R. Crager, 2012. "Generalizing the standardized hazard ratio to multivariate proportional hazards regression, with an application to clinical~genomic studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 399-417, May.
    4. Zuber Verena & Strimmer Korbinian, 2011. "High-Dimensional Regression and Variable Selection Using CAR Scores," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-27, July.
    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. Alain Chaney & Martin Hoesli, 2015. "Transaction-Based and Appraisal-Based Capitalization Rate Determinants," International Real Estate Review, Global Social Science Institute, vol. 18(1), pages 1-43.
    2. Kornelis J J van Hateren & Gijs W D Landman & Jarinke F H Arnold & Hanneke Joosten & Klaas H Groenier & Gerjan J Navis & Andrea Sparwasser & Stephan J L Bakker & Henk J G Bilo & Nanne Kleefstra, 2015. "Serum Proenkephalin A Levels and Mortality After Long-Term Follow-Up in Patients with Type 2 Diabetes Mellitus (ZODIAC-32)," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-10, July.
    3. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    4. Patrick Heagerty & Yingye Zheng, 2004. "Survival Model Predictive Accuracy and ROC Curves," UW Biostatistics Working Paper Series 1051, Berkeley Electronic Press.
    5. Wang Chamont & Gevertz Jana L., 2016. "Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 321-347, August.
    6. Matthias Schmid & Thomas Hielscher & Thomas Augustin & Olaf Gefeller, 2011. "A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error," Biometrics, The International Biometric Society, vol. 67(2), pages 524-535, June.
    7. Eleni†Rosalina Andrinopoulou & Paul H. C. Eilers & Johanna J. M. Takkenberg & Dimitris Rizopoulos, 2018. "Improved dynamic predictions from joint models of longitudinal and survival data with time†varying effects using P†splines," Biometrics, The International Biometric Society, vol. 74(2), pages 685-693, June.
    8. Barreto, Ikaro Daniel de Carvalho & Dore, Luiz Henrique & Stosic, Tatijana & Stosic, Borko D., 2021. "Extending DFA-based multiple linear regression inference: Application to acoustic impedance models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    9. Vicente Rios & Lisa Gianmoena, 2021. "On the link between temperature and regional COVID‐19 severity: Evidence from Italy," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(S1), pages 109-137, November.
    10. P. Saha & P. J. Heagerty, 2010. "Time-Dependent Predictive Accuracy in the Presence of Competing Risks," Biometrics, The International Biometric Society, vol. 66(4), pages 999-1011, December.
    11. Janez Stare & Maja Pohar Perme & Robin Henderson, 2011. "A Measure of Explained Variation for Event History Data," Biometrics, The International Biometric Society, vol. 67(3), pages 750-759, September.
    12. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    13. Yingye Zheng & Patrick J. Heagerty, 2005. "Partly Conditional Survival Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 61(2), pages 379-391, June.
    14. Paweł Teisseyre & Robert A. Kłopotek & Jan Mielniczuk, 2016. "Random Subspace Method for high-dimensional regression with the R package regRSM," Computational Statistics, Springer, vol. 31(3), pages 943-972, September.
    15. Name 1 Dieter Wang Email 1 & Iman (I.P.P.) van Lelyveld & Julia (J.) Schaumburg, 2018. "Do information contagion and business model similarities explain bank credit risk commonalities?," Tinbergen Institute Discussion Papers 18-100/IV, Tinbergen Institute.
    16. 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.
    17. Jialiang Li & Tonghui Yu & Jing Lv & Mei‐Ling Ting Lee, 2021. "Semiparametric model averaging prediction for lifetime data via hazards regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1187-1209, November.
    18. Tasadduq Imam & Kevin Tickle & Abdullahi Ahmed & William Guo, 2012. "Linear Relationship Between The Aud/Usd Exchange Rate And The Respective Stock Market Indices: A Computational Finance Perspective," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 19-42, January.
    19. Daniel Commenges & Benoit Liquet & Cécile Proust-Lima, 2012. "Choice of Prognostic Estimators in Joint Models by Estimating Differences of Expected Conditional Kullback–Leibler Risks," Biometrics, The International Biometric Society, vol. 68(2), pages 380-387, June.
    20. R. Schoop & E. Graf & M. Schumacher, 2008. "Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time-Dependent Covariates," Biometrics, The International Biometric Society, vol. 64(2), pages 603-610, June.

    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:spr:lifeda:v:26:y:2020:i:4:d:10.1007_s10985-020-09504-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.