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Biometrika 2003 90(2):341-353; doi:10.1093/biomet/90.2.341
© 2003 by Biometrika Trust
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Rank-based inference for the accelerated failure time model

Zhezhen Jin1, D.Y. Lin2, L.J. Wei3 and Zhiliang Ying4

1 Department of Biostatistics, Columbia University, New York, New York 10032, U.S.Azjin{at}biostat.cpmc.columbia.edu 2 Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A.lin{at}bios.unc.edu 3 Department of Biostatistics, Harvard University, Boston, Massachusetts 02115, U.S.A. wei{at}sdac.harvard.edu 4 Department of Statistics, Columbia University, New York, New York 10027, U.S.A. zying{at}stat.columbia.edu

A broad class of rank-based monotone estimating functions is developed for the semiparametric accelerated failure time model with censored observations.The corresponding estimators can be obtained via linear programming, and are shown to be consistent and asymptotically normal. The limiting covariance matrices can be estimated by a resampling technique, which does not involve nonparametric density estimation or numerical derivatives. The new estimators represent consistent roots of the non-monotone estimating equations based on the familiar weighted log-rank statistics. Simulation studies demonstrate that the proposed methods perform well in practical settings. Two real examples are provided.

Key Words: Accelerated life model; Censoring; Gehan statistic; Linear programming; Rank estimator; Survival data; Weighted log-rank statistic

Received August 2001. Revised November 2002


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