Marginal Regression Analysis for Semi-Competing Risks Data Under Dependent Censoring

被引：21

作者：

Ding, A. Adam ^{[1
]}

Shi, Guangkai ^{[1
]}

Wang, Weijing ^{[2
]}

Hsieh, Jin-Jian ^{[3
]}

机构：

[1] Northeastern Univ, Dept Math, Boston, MA 02115 USA

[2] Natl Chiao Tung Univ, Inst Stat, Hsinchu, Taiwan

[3] Natl Chung Cheng Univ, Dept Math, Taipei, Taiwan

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2009年 / 36卷 / 03期

关键词：

artificial censoring; log-rank statistic; multiple events data; transformation model; MODEL; RESIDUALS; CHECKING;

D O I：

10.1111/j.1467-9469.2008.00635.x

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Multiple events data are commonly seen in medical applications. There are two types of events, namely terminal and non-terminal. Statistical analysis for non-terminal events is complicated due to dependent censoring. Consequently, joint modelling and inference are often needed to avoid the problem of non-identifiability. This article considers regression analysis for multiple events data with major interest in a non-terminal event such as disease progression. We generalize the technique of artificial censoring, which is a popular way to handle dependent censoring, under flexible model assumptions on the two types of events. The proposed method is applied to analyse a data set of bone marrow transplantation.

引用

页码：481 / 500

页数：20

共 19 条

[1]

Andersen Per K, 2012, STAT MODELS BASED CO

[2] A two-sample comparison for multiple ordered event data [J].