응용통계연구 (The Korean Journal of Applied Statistics)

  • 제35권1호
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  • Pages.35-47
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  • 2022
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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생존 분석 자료에서 적용되는 시간 가변 ROC 분석에 대한 리뷰

Review for time-dependent ROC analysis under diverse survival models

  • 김양진 (숙명여자 대학교, 통계학과)
  • Kim, Yang-Jin (Department of Statistics, Sookmyung Women's University)
  • 투고 : 2021.09.01
  • 심사 : 2021.10.19
  • 발행 : 2022.02.28
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⟨ 이전 논문 다음 논문 ⟩

초록

Receiver operating characteristic (ROC) 곡선은 이항 반응 자료에 대한 마커의 분류 예측력을 측정하기 위해 널리 적용되어왔으며 최근에는 생존 분석에서도 매우 중요한 역할을 하고 있다. 여러 가지 유형의 중도 절단과 원인 불명 등 다양한 종류의 결측 자료를 포함한 생존 자료 분석에서 마커의 사건 발생 여부에 대한 예측력을 판단하기 위해 기존의 통계량을 확장하였다. 생존 분석 자료는 각 시점에서의 사건 발생 여부로 이해할 수 있으며, 따라서 시점마다 ROC 곡선과 AUC를 구할 수 있다. 본 논문에서는 우중도 절단과 경쟁 위험 모형하에서 사용되는 다양한 방법론과 관련 R 패키지를 소개하고 각 방법의 특성을 설명하고 비교하였으며 이를 검토하기 위해 간단한 모의실험을 시행하였다. 또한, 프랑스에서 수집된 치매 자료의 마커 분석을 시행하였다.

The receiver operating characteristic (ROC) curve was developed to quantify the classification ability of marker values (covariates) on the response variable and has been extended to survival data with diverse missing data structure. When survival data is understood as binary data (status of being alive or dead) at each time point, the ROC curve expressed at every time point results in time-dependent ROC curve and time-dependent area under curve (AUC). In particular, a follow-up study brings the change of cohort and incomplete data structures such as censoring and competing risk. In this paper, we review time-dependent ROC estimators under several contexts and perform simulation to check the performance of each estimators. We analyzed a dementia dataset to compare the prognostic power of markers.

키워드

과제정보

본 연구는 연구재단(NRF-2020R1A2C1A01100755)의 지원을 받았습니다.

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