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Biometrika Advance Access published online on June 25, 2009
Biometrika, doi:10.1093/biomet/asp025
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© 2009 Biometrika Trust

Article

Induced smoothing for the semiparametric accelerated failure time model: asymptotics and extensions to clustered data

Lynn M. Johnson

Department of Statistical Science, Cornell University, Ithaca, New York 14853, U.S.A. lms86{at}cornell.edu

Robert L. Strawderman

Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14853, U.S.A. rls54{at}cornell.edu

Received for publication 1 July 2008. Revision received 1 December 2008.
  Abstract

This paper extends the induced smoothing procedure of Brown & Wang (2006) for the semiparametric accelerated failure time model to the case of clustered failure time data. The resulting procedure permits fast and accurate computation of regression parameter estimates and standard errors using simple and widely available numerical methods, such as the Newton–Raphson algorithm. The regression parameter estimates are shown to be strongly consistent and asymptotically normal; in addition, we prove that the asymptotic distribution of the smoothed estimator coincides with that obtained without the use of smoothing. This establishes a key claim of Brown & Wang (2006) for the case of independent failure time data and also extends such results to the case of clustered data. Simulation results show that these smoothed estimates perform as well as those obtained using the best available methods at a fraction of the computational cost.

Key Words: Censoring • Convex optimization • Multivariate survival data • Rank regression


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