Fitting Cox’s Proportional Hazards Model Using Grouped Survival Data

Ian W. McKeague and Mei-Jie Zhang
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Ian W. McKeague: Florida State University, Department of Statistics
Mei-Jie Zhang: Florida State University, Department of Statistics

A chapter in Lifetime Data: Models in Reliability and Survival Analysis, 1996, pp 227-232 from Springer

Abstract: 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.

Keywords: Bias Correction; Calendar Period; Baseline Hazard Function; Asymptotic Bias; Large Sample Theory (search for similar items in EconPapers)
Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4757-5654-8_30

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DOI: 10.1007/978-1-4757-5654-8_30

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