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      A Frailty Model for Crossing-Hazard Data

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      https://www.riss.kr/link?id=A101601666

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      다국어 초록 (Multilingual Abstract)

      The crossing hazards are often observed in clinical trials on time-to-event. This indicates a strong non-proportional hazards (non-PH) structure and may occur due to a heterogeneity between survival data. Frailty, a random-effect term in hazard models, usually accounts such heterogeneity and the resulting marginal model, which is obtained by integrating out the frailty, leads to a non-PH model. Accordingly, in this paper we propose how to model the crossing hazards via a frailty and to fit the proposed model using SAS PROC NLMIXED. The proposed method is illustrated with a well-known data set from a randomized clinical trial on gastric cancer patients, where the aim of trial is to investigate the effect of chemotherapy (group 1) and combined chemotherapy and radiotherapy (group 2) on survival times of gastric cancer patients.
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      The crossing hazards are often observed in clinical trials on time-to-event. This indicates a strong non-proportional hazards (non-PH) structure and may occur due to a heterogeneity between survival data. Frailty, a random-effect term in hazard models...

      The crossing hazards are often observed in clinical trials on time-to-event. This indicates a strong non-proportional hazards (non-PH) structure and may occur due to a heterogeneity between survival data. Frailty, a random-effect term in hazard models, usually accounts such heterogeneity and the resulting marginal model, which is obtained by integrating out the frailty, leads to a non-PH model. Accordingly, in this paper we propose how to model the crossing hazards via a frailty and to fit the proposed model using SAS PROC NLMIXED. The proposed method is illustrated with a well-known data set from a randomized clinical trial on gastric cancer patients, where the aim of trial is to investigate the effect of chemotherapy (group 1) and combined chemotherapy and radiotherapy (group 2) on survival times of gastric cancer patients.

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      참고문헌 (Reference)

      1 Vaupel, J.W., "The impact of heterogeneity in individual frailty on the dynamics of mortality" 16 : 439-454, 1979

      2 Cox, D. R., "Regression models and life-tables(with discussion)" 34 : 187-220, 1972

      3 Hougaard, P., "Modelling heterogeneity in survival data" 28 : 695-701, 1991

      4 Ha, I. D., "Model selection for multi-component frailty models" 26 : 4790-4807, 2007

      5 Zeng, D., "Maximum likelihood estimation in semiparametric regression models with censored data (with discussion)" 69 : 507-564, 2007

      6 Aalen, O. O., "Heterogeneity in survival analysis" 7 : 1121-1137, 1988

      7 Ha, I. D., "Estimating frailty models via Poisson hierarchical generalized linear models" 12 : 663-681, 2003

      8 Schemper, M., "Cox analysis of survival data with non-proportional hazard function" 41 : 455-465, 1992

      9 Ha, I. D., "Comparison of hierarchical likelihood versus orthodox best linear unbiased predictor approaches for frailty models" 92 : 717-723, 2005

      10 Orbe, J., "Comparing proportional hazards and accelerated failure time models for survival analysis" 21 : 3493-3510, 2002

      1 Vaupel, J.W., "The impact of heterogeneity in individual frailty on the dynamics of mortality" 16 : 439-454, 1979

      2 Cox, D. R., "Regression models and life-tables(with discussion)" 34 : 187-220, 1972

      3 Hougaard, P., "Modelling heterogeneity in survival data" 28 : 695-701, 1991

      4 Ha, I. D., "Model selection for multi-component frailty models" 26 : 4790-4807, 2007

      5 Zeng, D., "Maximum likelihood estimation in semiparametric regression models with censored data (with discussion)" 69 : 507-564, 2007

      6 Aalen, O. O., "Heterogeneity in survival analysis" 7 : 1121-1137, 1988

      7 Ha, I. D., "Estimating frailty models via Poisson hierarchical generalized linear models" 12 : 663-681, 2003

      8 Schemper, M., "Cox analysis of survival data with non-proportional hazard function" 41 : 455-465, 1992

      9 Ha, I. D., "Comparison of hierarchical likelihood versus orthodox best linear unbiased predictor approaches for frailty models" 92 : 717-723, 2005

      10 Orbe, J., "Comparing proportional hazards and accelerated failure time models for survival analysis" 21 : 3493-3510, 2002

      11 Hougaard, P., "Analysis of multivariate survival data" Springer 2000

      12 IlDoHa, "Analysis of Tumor Recurrence Data Using AR(1) Frailty Model" 한국자료분석학회 5 (5): 811-820, 2003

      13 하일도, "Analysis of Survival Data with Cure Rate using Frailty" 한국자료분석학회 7 (7): 1915-1923, 2005

      14 하일도, "Analysis of Nested CGD Infection Data Using Multilevel Frailty Model" 한국자료분석학회 6 (6): 637-645, 2004

      15 하일도, "An AIC for Selecting Frailty Structures" 한국자료분석학회 7 (7): 41-47, 2005

      16 Stablein, D. M., "A two-sample test sensitivity to crossing hazard in uncensored and singly censored data" 41 : 643-652, 1985

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2026 평가예정 재인증평가 신청대상 (재인증)
      2020-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2017-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2013-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2004-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2002-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.26 1.26 1.15
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      1.05 0.98 0.956 0.4
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