Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations

被引:0
作者
Sy Han Chiou
Sangwook Kang
Junghi Kim
Jun Yan
机构
[1] University of Minnesota,Department of Mathematics and Statistics
[2] Duluth,Department of Applied Statistics
[3] Yonsei University,Division of Biostatistics
[4] University of Minnesota,Department of Statistics
[5] University of Connecticut,Center for Public Health and Health Policy Research
[6] University of Connecticut Health Center,Center for Environmental Sciences & Engineering
[7] University of Connecticut,undefined
来源
Lifetime Data Analysis | 2014年 / 20卷
关键词
Buckley-James estimator; Efficiency; Induced smoothing; Least squares; Multivariate survival;
D O I
暂无
中图分类号
学科分类号
摘要
The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rank-based estimator with Gehan’s weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the within-cluster dependence, especially when the within-cluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.
引用
收藏
页码:599 / 618
页数:19
相关论文
共 68 条
[1]  
Brown BM(2005)Standard errors and covariance matrices for smoothed rank estimators Biometrika 92 149-158
[2]  
Wang Y-G(2007)Induced smoothing for rank regression with censored survival times Stat Med 26 828-836
[3]  
Brown BM(1979)Linear regression with censored data Biometrika 66 429-436
[4]  
Wang Y-G(1972)Regression models and life-tables (with discussion) J R Stat Soc 34 187-220
[5]  
Buckley J(1976)Preliminary report on effects of photocoagulation therapy Am J Ophthalmol 81 383-396
[6]  
James I(1965)A generalized Wilcoxon test for comparing arbitrarily singly-censored samples Biometrika 52 203-223
[7]  
Cox DR(2006)The R package geepack for generalized estimating equations J Stat Softw 15 1-11
[8]  
Gehan EA(2002)Calibration regression of censored lifetime medical cost J Am Stat Assoc 97 318-327
[9]  
Halekoh U(1989)Modelling paired survival data with covariates Biometrics 45 145-156
[10]  
Højsgaard S(2003)Rank-based inference for the accelerated failure time model Biometrika 90 341-353