Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

A Class of Semiparametric Regression for the Accelerated Failure Time Model

Michael P. Jones
Biometrika
Vol. 84, No. 1 (Mar., 1997), pp. 73-84
Published by: Oxford University Press on behalf of Biometrika Trust
Stable URL: http://www.jstor.org/stable/2337556
Page Count: 12
  • Read Online (Free)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
A Class of Semiparametric Regression for the Accelerated Failure Time Model
Preview not available

Abstract

In this paper a general class of nonparametric test statistic, which includes both linear and nonlinear rank tests for the accelerated failure time model, is inverted into estimating equations for multiple regression. For right-censored data this general class of semiparametric regression procedures includes the linear rank estimators of Tsiatis (1990), extends the Theil-Sen estimator based on Kendall's t to multiple regression, and introduces several new families of regression methods based on inverting nonlinear rank tests. These new families include the weighted generalised logrank estimators and the weighted logit-rank estimators. Several estimators of the standard errors of the regression coefficients are given. The regression coefficient estimators are consistent and asymptotically normal with variances that can be consistently estimated. Several linear and nonlinear rank-based estimators of the regression parameters and several methods of estimating their standard errors and the corresponding confidence intervals are compared in a small sample simulation in settings with and without outliers among the covariates. In these simulations the generalised logrank estimators performed well as compared to the logrank estimators when there was no outlier among the covariates and had less bias than the logrank estimators when covariate outliers existed.

Page Thumbnails

  • Thumbnail: Page 
[73]
    [73]
  • Thumbnail: Page 
74
    74
  • Thumbnail: Page 
75
    75
  • Thumbnail: Page 
76
    76
  • Thumbnail: Page 
77
    77
  • Thumbnail: Page 
78
    78
  • Thumbnail: Page 
79
    79
  • Thumbnail: Page 
80
    80
  • Thumbnail: Page 
81
    81
  • Thumbnail: Page 
82
    82
  • Thumbnail: Page 
83
    83
  • Thumbnail: Page 
84
    84