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Fitting Cox’s Proportional Hazards Model Using Grouped Survival Data

In: Lifetime Data: Models in Reliability and Survival Analysis

Author

Listed:
  • Ian W. McKeague

    (Florida State University, Department of Statistics
    Medical College of Wisconsin, Division of Biostatistics)

  • Mei-Jie Zhang

    (Florida State University, Department of Statistics
    Medical College of Wisconsin, Division of Biostatistics)

Abstract

Cox’s proportional hazard model is often fit to grouped survival data (i.e., occurrence and exposure data over various specified time-intervals and covariate bins), as opposed to continuous data. The practical limits to using such data for inference in the Cox model are investigated. A large sample theory, allowing the bins and time-intervals to shrink as the sample size increase, is developed. It turns out that the usual estimator of the regression parameter is asymptotically biased under optimal rates of convergence. The asymptotic bias is found, and an assessment of the effect on inference is given.

Suggested Citation

  • Ian W. McKeague & Mei-Jie Zhang, 1996. "Fitting Cox’s Proportional Hazards Model Using Grouped Survival Data," Springer Books, in: Nicholas P. Jewell & Alan C. Kimber & Mei-Ling Ting Lee & G. A. Whitmore (ed.), Lifetime Data: Models in Reliability and Survival Analysis, pages 227-232, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4757-5654-8_30
    DOI: 10.1007/978-1-4757-5654-8_30
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