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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
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    References listed on IDEAS

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    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.
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    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.

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