You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Commentary on Andersen and Gill's "Cox's Regression Model for Counting Processes: A Large Sample Study"
Steven G. Self and Ross L. Prentice
The Annals of Statistics
Vol. 10, No. 4 (Dec., 1982), pp. 1121-1124
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2240715
Page Count: 4
You can always find the topics here!Topics: Regression analysis, Censorship, Martingales, Linear regression, Statism, Asymptotic theory, Information economics, Factorization, Parametric models, Time dependence
Were these topics helpful?See something inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
In this issue Andersen and Gill (hereafter AG) present a stimulating development of asymptotic distribution theory for the Cox regression model with time-dependent covariates. They use a counting process formulation for the failure time data and martingale covergence results. This approach involves such conditions as σ-algebra right continuity and predictable, locally bounded, covariate processes. In this commentary we consider the implications of such assumptions for likelihood factorization and covariate modeling. In particular, it is noted that the partial likelihood function modeled by AG cannot, in general, involve covariate measurements at the random failure times. Some related work by the authors on a partial likelihood function that may involve covariate values at the random failure times is briefly discussed. Assumptions under which the intensity process modeled by AG has a standard "hazard" function interpretation are described and some generalizations of the AG results are mentioned.
The Annals of Statistics © 1982 Institute of Mathematical Statistics