Induced smoothing for rank regression with censored survival times

Statistics in Medicine
B M Brown, You-Gan Wang

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.

References


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Citations

Aug 21, 2013·Statistics and Computing·Matthias ChungBrent A Johnson
Oct 11, 2012·Computational Statistics & Data Analysis·Haifen LiYincai Tang
Jul 5, 2007·Biometrics·You-Gan Wang, Yudong Zhao
Apr 18, 2018·Statistics in Medicine·Jennifer A Sinnott, Tianxi Cai
Jul 7, 2018·Biometrical Journal. Biometrische Zeitschrift·Sangbum ChoiXuelin Huang
Nov 6, 2018·Lifetime Data Analysis·Ritesh RamchandaniDavid A Schoenfeld
Nov 25, 2017·Statistics in Medicine·Chi Hyun LeeXianghua Luo
Jan 24, 2018·Journal of the Royal Statistical Society. Series B, Statistical Methodology·Caiyun FanYong Zhou
Dec 6, 2017·Statistics in Medicine·Tianmeng LyuChiung-Yu Huang
Oct 13, 2020·Lifetime Data Analysis·Jichang YuJianwen Cai
Sep 5, 2021·Biometrics·Byungtae Seo, Sangwook Kang

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