Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Analysis of multi-center bladder cancer survival data using variable-selection method of multi-level frailty models

다수준 프레일티모형 변수선택법을 이용한 다기관 방광암 생존자료분석

  • Kim, Bohyeon (Clinical Trial Center, Busan Paik Hospital of Inje University) ;
  • Ha, Il Do (Department of Statistics, Pukyong National University) ;
  • Lee, Donghwan (Department of Statistics, Ewha Womans University)
  • 김보현 (인제대학교 부산백병원 임상시험센터) ;
  • 하일도 (부경대학교 통계학과) ;
  • 이동환 (이화여자대학교 통계학과)
  • Received : 2016.02.04
  • Accepted : 2016.03.21
  • Published : 2016.03.31
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Abstract

It is very important to select relevant variables in regression models for survival analysis. In this paper, we introduce a penalized variable-selection procedure in multi-level frailty models based on the "frailtyHL" R package (Ha et al., 2012). Here, the estimation procedure of models is based on the penalized hierarchical likelihood, and three penalty functions (LASSO, SCAD and HL) are considered. The proposed methods are illustrated with multi-country/multi-center bladder cancer survival data from the EORTC in Belgium. We compare the results of three variable-selection methods and discuss their advantages and disadvantages. In particular, the results of data analysis showed that the SCAD and HL methods select well important variables than in the LASSO method.

생존분석 회귀모형에서 적절한 변수를 선택하는 것은 매우 중요하다. 본 논문에서는 "frailtyHL" R 패키지 (Ha 등, 2012)를 기반으로 하여 다수준 프레일티 모형 (multi-level frailty models)에서 벌점화 변수선택 방법 (penalized variable-selection method)의 절차를 소개한다. 여기서 모형 추정은 벌점화 다단계 가능도에 기초하며, 세 가지 벌점 함수 (LASSO, SCAD 및 HL)가 고려된다. 개발된 방법의 예증을 위해 벨기에 EORTC (European Organization for Research and Treatment of Cancer; 유럽 암 치료기구)에서 수행된 다국가/다기관 임상시험 자료를 이용하여 세 가지 변수 선택 방법의 결과를 비교하고, 그 결과들의 상대적 장 단점에 대해 토론한다. 특히, 자료 분석 결과에 의하면 SCAD와 HL방법이 LASSO보다 중요한 변수를 잘 선택하는 것으로 나타났다.

Keywords

References

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