IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v38y2011i10p2089-2097.html
   My bibliography  Save this article

Assessing the adequacy of Weibull survival models: a simulated envelope approach

Author

Listed:
  • Yun Zhao
  • Andy Lee
  • Kelvin Yau
  • Geoffrey McLachlan

Abstract

The Weibull proportional hazards model is commonly used for analysing survival data. However, formal tests of model adequacy are still lacking. It is well known that residual-based goodness-of-fit measures are inappropriate for censored data. In this paper, a graphical diagnostic plot of Cox-Snell residuals with a simulated envelope added is proposed to assess the adequacy of Weibull survival models. Both single component and two-component mixture models with random effects are considered for recurrent failure time data. The effectiveness of the diagnostic method is illustrated using simulated data sets and data on recurrent urinary tract infections of elderly women.

Suggested Citation

  • Yun Zhao & Andy Lee & Kelvin Yau & Geoffrey McLachlan, 2011. "Assessing the adequacy of Weibull survival models: a simulated envelope approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2089-2097.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2089-2097
    DOI: 10.1080/02664763.2010.545115
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545115
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2010.545115?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. Xiang, Liming & Yau, Kelvin K.W. & Tse, S.K. & Lee, Andy H., 2007. "Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5977-5993, August.
    2. Michael Parzen & Stuart R. Lipsitz, 1999. "A Global Goodness-of-Fit Statistic for Cox Regression Models," Biometrics, The International Biometric Society, vol. 55(2), pages 580-584, June.
    3. Lee, Andy H. & Fung, Wing K., 1997. "Confirmation of multiple outliers in generalized linear and nonlinear regressions," Computational Statistics & Data Analysis, Elsevier, vol. 25(1), pages 55-65, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Josmar Mazucheli & Bruna Alves & Mustafa Ç. Korkmaz & Víctor Leiva, 2022. "Vasicek Quantile and Mean Regression Models for Bounded Data: New Formulation, Mathematical Derivations, and Numerical Applications," Mathematics, MDPI, vol. 10(9), pages 1-23, April.

    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. Xiang, Liming & Tse, Siu-Keung & Lee, Andy H., 2002. "Influence diagnostics for generalized linear mixed models: applications to clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 759-774, October.
    2. P. Economou & S. Malefaki & C. Caroni, 2015. "Bayesian Threshold Regression Model with Random Effects for Recurrent Events," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 871-898, December.
    3. Shiyuan Chen & Sally Wallace, 2008. "Determinants of Education Duration in Jamaica," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper0803, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    4. Lai, Xin & Yau, Kelvin K.W., 2010. "Extending the long-term survivor mixture model with random effects for clustered survival data," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2103-2112, September.
    5. Akouemo, Hermine N. & Povinelli, Richard J., 2016. "Probabilistic anomaly detection in natural gas time series data," International Journal of Forecasting, Elsevier, vol. 32(3), pages 948-956.
    6. Iing Lukman, 2023. "Agriculturally based culinary businesses have been spread across the country nowadays, included in Indonesia. The objective of this paper is to deal with the survival analysis of those kinds’ busine," Technium Business and Management, Technium Science, vol. 6(1), pages 88-104.
    7. Yick, John S. & Lee, Andy H., 1998. "Unmasking outliers in two-way contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 29(1), pages 69-79, November.

    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:taf:japsta:v:38:y:2011:i:10:p:2089-2097. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.