Rank Estimation of Accelerated Lifetime Models With Dependent Censoring

Under independent censoring, estimation of the covariate effects in the accelerated lifetime model may be based on censored data rank tests. Similar rank methodology has been developed with bivariate accelerated lifetime models for dependent censoring but uses artificial censoring, which may lead to substantial information loss. We present a new artificial censoring technique using pairwise ranking and establish the asymptotic properties of a pairwise rank estimator. Simulations show that the pairwise approach achieves large reductions in artificial censoring and large efficiency gains over the existing rank estimator. The simulations evidence moderate efficiency gains under independent censoring over a rank estimator that is semiparametric efficient under independent censoring. An AIDS data analysis illustrates the practical utility of the inferential procedures.

[1]  Debashis Ghosh,et al.  Semiparametric analysis of recurrent events data in the presence of dependent censoring. , 2003, Biometrics.

[2]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[3]  Weijing Wang,et al.  Estimating the association parameter for copula models under dependent censoring , 2003 .

[4]  John P. Klein,et al.  Estimates of marginal survival for dependent competing risks based on an assumed copula , 1995 .

[5]  Michael G. Akritas,et al.  On the Use of Nonparametric Regression Techniques for Fitting Parametric Regression Models , 1996 .

[6]  James M. Robins,et al.  An analytic method for randomized trials with informative censoring: Part II , 1995, Lifetime data analysis.

[7]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[8]  Susan A. Murphy,et al.  The Theil-Sen Estimator with Doubly Censored Data and Applications to Astronomy , 1995 .

[9]  A. Tsiatis.  Estimating Regression Parameters Using Linear Rank Tests for Censored Data , 1990 .

[10]  Z. Ying,et al.  A resampling method based on pivotal estimating functions , 1994 .

[11]  Bo E. Honoré,et al.  Pairwise difference estimators of censored and truncated regression models , 1994 .

[12]  Zhiliang Ying,et al.  A Large Sample Study of Rank Estimation for Censored Regression Data , 1993 .

[13]  James M. Robins,et al.  Comparing two failure time distributions in the presence of dependent censoring , 1996 .

[14]  Joseph D. Conklin.  Classical Competing Risks , 2002, Technometrics.

[15]  W. Hoeffding.  A Class of Statistics with Asymptotically Normal Distribution , 1948 .

[16]  Zhiliang Ying,et al.  Semiparametric regression analysis of longitudinal data with informative drop-outs. , 2003, Biostatistics.

[17]  M. Joffe,et al.  Administrative and artificial censoring in censored regression models , 2001, Statistics in medicine.

[18]  Jason P. Fine,et al.  On semi-competing risks data , 2001 .

[19]  S. Hammer,et al.  Nelfinavir, efavirenz, or both after the failure of nucleoside treatment of HIV infection. , 2001, The New England journal of medicine.

[20]  M. Akritas,et al.  Nonparametric inference in factorial designs with censored data. , 1996, Biometrics.

[21]  Jason P Fine,et al.  Estimating the distribution of nonterminal event time in the presence of mortality or informative dropout. , 2003, Controlled clinical trials.

[22]  Zhiliang Ying,et al.  Linear regression analysis of censored survival data based on rank tests , 1990 .

[23]  Eric V. Slud,et al.  Dependent competing risks and summary survival curves , 1983 .

[24]  James J. Heckman,et al.  The identifiability of the competing risks model , 1989 .