Analysis of Tumor Recurrence Data Using AR(1) Frailty Model

• 발행기관: 한국자료분석학회
• 저자명: Il Do Ha Geon-Ho Cho
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.5 No.4, 811~819쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2003.12.30

영문 초록

Frailty models have been widely used for analyzing multivariate survival data such as recurrent or multiple event data. We analyze the tumor recurrence data(Gail et al., 1980), which show the gap times between successive mammary tumor occurrences in a subject, using a serially correlated AR(1) frailty model. Here, the frailty of each subject is not constant, but changes stochastically over the gap times. Inferences are based on hierarchical-likelihood, which provides a simple unified framework for various random-effect models. In particular, we demonstrate that an AIC(Akaike information criterion) based on hierarchical-likelihood selects the AR(1) frailty model as an appropriate model for the tumor data.

목차

1. Introduction

2. Tumor Recurrence Data

3. Estimation of AR(1) Frailty Model

4. Analysis of Tumor Recurrent Data

5. Conclusion

References

키워드

Frailty models, Hierarchical-likelihood, Model selection, Recurrent event data, AR(1) frailty