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Title   COX 모델을 이용한 생존자료분석에서 Proportionality 가정 검정 의의 ( Proportionality Assumption Test of Cos's Proportional Hazards Model in Survival Analysis )
Publicationinfo   1991 Jan; 023(04): 852-860.
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Abstract   Survival analysis using Cox's proportional hazards model is widely used to search for prog- nostic factors or covariates for which the time-to-response is influenced by other explanatory variables. This technique presumes death rates may be modelled as both log-linear functions of the covariates and proportionality of hazard ratios. It is essential to test those assumptions whenever multivariate modelling procedure is applied to any given data. In case of application to Coxs model, the proportionality assumption should be secured to draw out valid estimate of each prognostic factor. It can be done through checking LLS (log(-log survival function)) plot of each variable, which enables to verify parallelism between cumulative survival functions according to the level of exposure. Survival data on 86 histologically confirmed breast cancer cases at the Seoul National University Hospital, 1984-1988, were used in this analysis. Prognostic factors as age at diagnosis, tumor size, lymph node involvement, clinical stage by TNM classificetion, and pathological grade were dichotomized for the study purpose. A covariate, i.e. age at diagnosis, even though it was well-fitted to the Coxs model, did not resch to a significant level by both ordinary statistical methods and the test for significance by Coxs model. However, lymph node involvement as well as clinical stage were revealed to be significant prognostic factors, in which proportionality assumption were satisfied. Other variables, which did not show any evidence of parallelism, even crossing each other, were statistically non-significant by both ordinary tests and Coxs method. It suggests that statistical significance in survival data analysis was affected by real difference of survivalship of each prognostic factor, as well as by the fitness to the Caxs proportional hazsrds model, which can be easily assessed by a simple plotting method. Test for assumption of proprtionality is recommended in order to get a more valid estimate of relative risk, and may be valuable to expand biological knowledge on the etiology of a disease under investigation.
저 자   이무송(Moo Song Lee),유근영(Byung Joo Park),노동영(Dong Young Noh),최국진(Kuk Jin Choe)