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Fitting Cox's Regression Model to Survival Data Using Glim

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  • John Whitehead

Abstract

The proportional hazard regression model is reviewed, and its analysis using GLIM is described. Methods of estimating the underlying survivor functions are discussed. The Poisson model which allows the use of GLIM is introduced and interpreted. Two different treatments of tied observations are mentioned, and their properties are compared in the context of a specific example.

Suggested Citation

  • John Whitehead, 1980. "Fitting Cox's Regression Model to Survival Data Using Glim," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(3), pages 268-275, November.
    • Handle: RePEc:bla:jorssc:v:29:y:1980:i:3:p:268-275
    DOI: 10.2307/2346901
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    Cited by:

    1. 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).
    2. Peter C. Austin, 2017. "A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications," International Statistical Review, International Statistical Institute, vol. 85(2), pages 185-203, August.
    3. Ma, Jun & Heritier, Stephane & Lô, Serigne N., 2014. "On the maximum penalized likelihood approach for proportional hazard models with right censored survival data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 142-156.
    4. Luis Rosero-Bixby, 2008. "The exceptionally high life expectancy of Costa Rican nonagenarians," Demography, Springer;Population Association of America (PAA), vol. 45(3), pages 673-691, August.
    5. David Oakes, 2023. "Cox (1972): recollections and reflections," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 699-708, October.
    6. Christopher J. Boudreaux, 2021. "Employee compensation and new venture performance: does benefit type matter?," Small Business Economics, Springer, vol. 57(3), pages 1453-1477, October.
    7. Simon N. Wood, 2020. "Rejoinder on: Inference and computation with Generalized Additive Models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 354-358, June.
    8. Verena Bauer & Dietmar Harhoff & Göran Kauermann, 2022. "A smooth dynamic network model for patent collaboration data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 97-116, March.
    9. Gifford, Elizabeth J. & Wells, Rebecca S. & Bai, Yu & Malone, Patrick S., 2015. "Is implementation fidelity associated with improved access to care in a School-based Child and Family Team model?," Evaluation and Program Planning, Elsevier, vol. 49(C), pages 41-49.
    10. 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.
    11. Trond Petersen, 1986. "Estimating Fully Parametric Hazard Rate Models with Time-Dependent Covariates," Sociological Methods & Research, , vol. 14(3), pages 219-246, February.
    12. Maaya, Leonard & Meulders, Michel & Vandebroek, Martina, 2021. "Joint analysis of preferences and drop out data in discrete choice experiments," Journal of choice modelling, Elsevier, vol. 41(C).
    13. Breen, Richard, 1991. "Education, Employment And Training In The Youth Labour Market," Research Series, Economic and Social Research Institute (ESRI), number GRS152, June.
    14. Song, Shige & Wang, Wei & Hu, Peifeng, 2009. "Famine, death, and madness: Schizophrenia in early adulthood after prenatal exposure to the Chinese Great Leap Forward Famine," Social Science & Medicine, Elsevier, vol. 68(7), pages 1315-1321, April.

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