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A Two‐Step Regression Model for Hazard Functions

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  • J. A. Anderson
  • A. Senthilselvan

Abstract

The regression model for proportional hazards (Cox, 1972) is extended to allow (approximately) for time‐varying covariate coefficients. This is particularly appropriate for cancer mortality studies where the effect of a covariate measured at one point of time decays as time progresses. A smoothed hazard function estimate for this extended model is derived using penalized maximum likelihood. A case‐history is given concerned with recurrence of carcinoma of the vulva. The basic model does not fit very well, but the extended model fits well.

Suggested Citation

  • J. A. Anderson & A. Senthilselvan, 1982. "A Two‐Step Regression Model for Hazard Functions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(1), pages 44-51, March.
    • Handle: RePEc:bla:jorssc:v:31:y:1982:i:1:p:44-51
    DOI: 10.2307/2347073
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    Cited by:

    1. Aurelija Burinskienė & Milena Seržantė, 2022. "Digitalisation as the Indicator of the Evidence of Sustainability in the European Union," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    2. John O'Quigley & Loki Natarajan, 2004. "Erosion of Regression Effect in a Survival Study," Biometrics, The International Biometric Society, vol. 60(2), pages 344-351, June.
    3. A Syamsundar & V N A Naikan, 2008. "A proportional intensity segmented model for maintained systems," Journal of Risk and Reliability, , vol. 222(4), pages 643-654, December.
    4. Ronghui Xu & Sudeshna Adak, 2002. "Survival Analysis with Time-Varying Regression Effects Using a Tree-Based Approach," Biometrics, The International Biometric Society, vol. 58(2), pages 305-315, June.
    5. Ian W. McKeague & Mourad Tighiouart, 2000. "Bayesian Estimators for Conditional Hazard Functions," Biometrics, The International Biometric Society, vol. 56(4), pages 1007-1015, December.

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