Lifetime Data Analysis

, Volume 12, Issue 3, pp 345–364 | Cite as

A hybrid Newton-type method for censored survival data using double weights in linear models

  • Menggang Yu
  • Bin Nan


As an alternative to the Cox model, the rank-based estimating method for censored survival data has been studied extensively since it was proposed by Tsiatis [Tsiatis AA (1990) Ann Stat 18:354–372] among others. Due to the discontinuity feature of the estimating function, a significant amount of work in the literature has been focused on numerical issues. In this article, we consider the computational aspects of a family of doubly weighted rank-based estimating functions. This family is rich enough to include both estimating functions of Tsiatis (1990) for the randomly observed data and of Nan et al. [Nan B, Yu M, Kalbfleisch JD (2006) Biometrika (to appear)] for the case-cohort data as special examples. The latter belongs to the biased sampling problems. We show that the doubly weighted rank-based discontinuous estimating functions are monotone, a property established for the randomly observed data in the literature, when the generalized Gehan-type weights are used. Though the estimating problem can be formulated to a linear programming problem as that for the randomly observed data, due to its easily uncontrollable large scale even for a moderate sample size, we instead propose a Newton-type iterated method to search for an approximate solution of the (system of) discontinuous monotone estimating equation(s). Simulation results provide a good demonstration of the proposed method. We also apply our method to a real data example.


Censored linear regression Double weights Two-stage design Case-cohort design Hybrid Newton-type method Generalized Gehan-type weights Monotone estimating function Linear programming 



We thank Drs. Howard M. Sandler and Jeremy M. G. Taylor for providing the prostate cancer data.


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Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of Medicine/BiostatisticsIndiana UniversityIndianapolisUSA
  2. 2.Department of BiostatisticsUniversity of MichiganAnn ArborUSA

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