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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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Purpose: The purpose of this study is to point out that the Kaplan-Meier method is not valid to calculate the survival probability or failure probability (risk) in the presence of competing risks and to introduce more valid method of cumulative incidence function.
Methods: Survival analysis methods have been widely used in biostatistics division. However the same methods have not been utilized in reliability division. Especially competing risks cases, where several causes of failure occur and the occurrence of one event precludes the occurrence of the other events, are scattered in reliability field. But they are not noticed in the realm of reliability expertism or they are analysed in the wrong way. Specifically Kaplan-Meier method which assumes that the censoring times and failure times are independent is used to calculate the probability of failure in the presence of competing risks, thereby overestimating the real probability of failure. Hence, cumulative incidence function is introduced and sample competing risks data are analysed using cumulative incidence function and some graphs. Finally comparison of cumulative incidence functions and regression type analysis are mentioned briefly.
Results: Cumulative incidence function is used to calculate the survival probability or failure probability (risk) in the presence of competing risks and some useful graphs depicting the failure trend over the lifetime are introduced.
Conclusion: This paper shows that Kaplan-Meier method is not appropriate for the evaluation of survival or failure over the course of lifetime. In stead, cumulative incidence function is shown to be useful. Some graphs using the cumulative incidence functions are also shown to be informative.
Methods: Survival analysis methods have been widely used in biostatistics division. However the same methods have not been utilized in reliability division. Especially competing risks cases, where several causes of failure occur and the occurrence of one event precludes the occurrence of the other events, are scattered in reliability field. But they are not noticed in the realm of reliability expertism or they are analysed in the wrong way. Specifically Kaplan-Meier method which assumes that the censoring times and failure times are independent is used to calculate the probability of failure in the presence of competing risks, thereby overestimating the real probability of failure. Hence, cumulative incidence function is introduced and sample competing risks data are analysed using cumulative incidence function and some graphs. Finally comparison of cumulative incidence functions and regression type analysis are mentioned briefly.
Results: Cumulative incidence function is used to calculate the survival probability or failure probability (risk) in the presence of competing risks and some useful graphs depicting the failure trend over the lifetime are introduced.
Conclusion: This paper shows that Kaplan-Meier method is not appropriate for the evaluation of survival or failure over the course of lifetime. In stead, cumulative incidence function is shown to be useful. Some graphs using the cumulative incidence functions are also shown to be informative.
목차
1. 서론
2. Kaplan-Meier 방법과 이 방법의 문제점
3. 경쟁적 위험하에서 Cumulative Incidence Function 구하기
4. 결론 및 논의
References
참고문헌 (13)
참고문헌 신청
Klein, J. P. and Moeschberger, M. L. (2005). “Survival analysis: techniques for censored and truncated data”. Springer Science & Business Media.
Kalbfleisch, J. D. and Prentice, R. L. (2011). “The statistical analysis of failure time data”. John Wiley & Sons.
Porta Bleda, N., Gómez Melis, G. and Calle Rosingana, M. L. (2008). “The role of survival functions in competing risks”.
Putter, H., Fiocco, M. and Geskus, R. B. (2007). “Tutorial in biostatistics: competing risks and multi‐ state models”. Statistics in medicine, Vol. 26, No. 11, pp. 2389-2430.
Kim, H. T. (2007). “Cumulative incidence in competing risks data and competing risks regression analysis”. Clinical Cancer Research, Vol. 13, No. 2, pp. 559-565.
Kalbfleisch, J. D. and Prentice, R. L. (2011). “The statistical analysis of failure time data”. John Wiley & Sons.
Porta Bleda, N., Gómez Melis, G. and Calle Rosingana, M. L. (2008). “The role of survival functions in competing risks”.
Putter, H., Fiocco, M. and Geskus, R. B. (2007). “Tutorial in biostatistics: competing risks and multi‐ state models”. Statistics in medicine, Vol. 26, No. 11, pp. 2389-2430.
Kim, H. T. (2007). “Cumulative incidence in competing risks data and competing risks regression analysis”. Clinical Cancer Research, Vol. 13, No. 2, pp. 559-565.
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UCI(KEPA) : I410-ECN-0101-2016-323-002731258