Sieve Estimation for the Cox Model with Clustered Interval-Censored Failure Time Data

被引：5

作者：

Li J. ^{[1
]}

Tong X. ^{[2
]}

Sun J. ^{[3
]}

机构：

[1] Department of Biostatistics, Harvard University, Boston, MA

[2] School of Mathematical Sciences, Beijing Normal University, Beijing

[3] Department of Statistics, University of Missouri, Columbia, MO

来源：

Statistics in Biosciences | 2014年 / 6卷 / 1期

基金：

美国国家卫生研究院; 中国国家自然科学基金; 美国国家科学基金会;

关键词：

Clustered data; EM algorithm; Frailty model; Interval-censoring; Sieve maximum likelihood estimation;

D O I：

10.1007/s12561-012-9078-1

中图分类号：

学科分类号：

摘要：

Clustered interval-censored failure time data occur when the failure times of interest are clustered into small groups and known only to lie in certain intervals. A number of methods have been proposed for regression analysis of clustered failure time data, but most of them apply only to clustered right-censored data. In this paper, a sieve estimation procedure is proposed for fitting a Cox frailty model to clustered interval-censored failure time data. In particular, a two-step algorithm for parameter estimation is developed and the asymptotic properties of the resulting sieve maximum likelihood estimators are established. The finite sample properties of the proposed estimators are investigated through a simulation study and the method is illustrated by the data arising from a lymphatic filariasis study. © 2012 International Chinese Statistical Association.

引用

页码：55 / 72

页数：17

共 18 条

[1]

Bellamy S.L., Li Y., Ryan L.M., Lipsitz S., Canner M.J., Wright R., Analysis of clustered and interval censored data from a community-based study in asthma, Stat Med, 23, pp. 3607-3621, (2004)

[2]

Cai J., Prentice R.L., Estimating equations for hazard ratio parameters based on correlated failure time data, Biometrika, 82, pp. 151-164, (1995)

[3]

Cai J., Prentice R.L., Regression estimation using multivariate failure time data and a common baseline hazard function model, Lifetime Data Anal, 3, pp. 197-213, (1997)

[4]

Cai J., Sen P., Zhou H., A random effects model for multivariate failure time data from multicenter clinical trials, Biometrics, 55, pp. 182-189, (1999)

[5]

Cai T., Wei L., Wilcox M., Semi-parametric regression analysis for clustered failure time data, Biometrika, 87, pp. 867-878, (2000)

[6]

Chen K., Tong X., Varying coefficient transformation models with censored data, Biometrika, 97, pp. 969-976, (2010)

[7]

Clayton D.G., Cuzick J., Multivariate generalizations of the proportional hazards model, J R Stat Soc, 148, pp. 82-117, (1985)

[8]

Dudley R.M., A Course on Empirical Processes, ècole D' ètè De probabilitès De St Flour, 1097, (1984)

[9]

Guo G., Rodriguez G., Estimating a multivariate proportional hazards model for clustered data using the em algorithm, with an application to child survival in Guatemala, J Am Stat Assoc, 97, pp. 969-976, (1992)

[10]

Hougaard P., Analysis of Multivariate Survival Data: Statistics for Biology and Health, (2000)

← 1 2 →