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The Fitting of Exponential, Weibull and Extreme Value Distributions to Complex Censored Survival Data Using Glim

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  • Murray Aitkin
  • David Clayton

Abstract

Regression models may be fitted to censored survival data by the use of exponential, Weibull and extreme value distributions in glim. Standard probability plotting procedures for uncensored data may be modified to allow for censoring. A simultaneous test procedure may be used to determine a minimal adequate regression model. The procedure is briefly illustrated on two sets of published cancer survival data.

Suggested Citation

  • Murray Aitkin & David Clayton, 1980. "The Fitting of Exponential, Weibull and Extreme Value Distributions to Complex Censored Survival Data Using Glim," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 156-163, June.
    • Handle: RePEc:bla:jorssc:v:29:y:1980:i:2:p:156-163
    DOI: 10.2307/2986301
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    Citations

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    Cited by:

    1. Irit Aitkin, "undated". "The effect of missing data on covariates in survival analysis," Australasian Stata Users' Group Meetings 2004 6, Stata Users Group.
    2. Orbe Lizundia, Jesús María, 2000. "Un Modelo Lineal Generalizado Semiparametrico para Analisis de Duracion con Censura," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    3. A. J. Scallan, 1999. "Fitting a mixture distribution to complex censored survival data using generalized linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(6), pages 747-753.
    4. Orbe, Jesus & Nunez-Anton, Vicente, 2006. "Alternative approaches to study lifetime data under different scenarios: from the PH to the modified semiparametric AFT model," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1565-1582, March.
    5. A. J. Scallan, 1999. "Regression modelling of interval-censored failure time data using the Weibull distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(5), pages 613-618.
    6. Powers, Daniel A. & Yun, Myeong-Su, 2009. "Multivariate Decomposition for Hazard Rate Models," IZA Discussion Papers 3971, Institute of Labor Economics (IZA).
    7. M. M. Shoukri & M. Attanasio & J. M. Sargeant, 1998. "Parametric versus semi-parametric models for the analysis of correlated survival data: A case study in veterinary epidemiology," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(3), pages 357-374.
    8. Hanley James A & Miettinen Olli S, 2009. "Fitting Smooth-in-Time Prognostic Risk Functions via Logistic Regression," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-25, January.
    9. Thomas R. Fleming & D. Y. Lin, 2000. "Survival Analysis in Clinical Trials: Past Developments and Future Directions," Biometrics, The International Biometric Society, vol. 56(4), pages 971-983, December.
    10. Trond Petersen, 1986. "Estimating Fully Parametric Hazard Rate Models with Time-Dependent Covariates," Sociological Methods & Research, , vol. 14(3), pages 219-246, February.
    11. Fuentes, R. & Dwyer-Joyce, R.S. & Marshall, M.B. & Wheals, J. & Cross, E.J., 2020. "Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling," Renewable Energy, Elsevier, vol. 147(P1), pages 776-797.

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