Please use this identifier to cite or link to this item: https://doi.org/10.1002/sim.2576
Title: Induced smoothing for rank regression with censored survival times
Authors: Brown, B.M. 
Wang, Y.-G.
Keywords: Accelerated failure time model
Censored data
Covariance estimates
Gehan-Wilcoxon estimates
Induced smoothing
Monotone estimating functions
Rank regression
Standard errors
Issue Date: 20-Feb-2007
Source: Brown, B.M., Wang, Y.-G. (2007-02-20). Induced smoothing for rank regression with censored survival times. Statistics in Medicine 26 (4) : 828-836. ScholarBank@NUS Repository. https://doi.org/10.1002/sim.2576
Abstract: Adaptions of weighted rank regression to the accelerated failure time model for censored survival data have been successful in yielding asymptotically normal estimates and flexible weighting schemes to increase statistical efficiencies. However, for only one simple weighting scheme, Gehan or Wilcoxon weights, are estimating equations guaranteed to be monotone in parameter components, and even in this case are step functions, requiring the equivalent of linear programming for computation. The lack of smoothness makes standard error or covariance matrix estimation even more difficult. An induced smoothing technique overcame these difficulties in various problems involving monotone but pure jump estimating equations, including conventional rank regression. The present paper applies induced smoothing to the Gehan-Wilcoxon weighted rank regression for the accelerated failure time model, for the more difficult case of survival time data subject to censoring, where the inapplicability of permutation arguments necessitates a new method of estimating null variance of estimating functions. Smooth monotone parameter estimation and rapid, reliable standard error or covariance matrix estimation is obtained. Copyright © 2006 John Wiley & Sons, Ltd.
Source Title: Statistics in Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/132718
ISSN: 02776715
DOI: 10.1002/sim.2576
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