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A case-control study was conducted to investigate protective effect of breast feeding against breast cancer in Korea. Included in the analysis were 162 women, identified as having a histologically-confirmed diagnosis of breast cancer in a hospitaL The 150 hospital-contrals were women free of any malignant neoplasms nor other systematic diseases. Randomly se- lected neighborhoods and friends of the patients(n= 149) were also included as community controls in the analysis. Information on risk factors of breast cancer was collected by self- administered questionnaire. Statistical adjustment for potential confounders as well as a likeli- hood ratio test for trend was done by multivariate linear logistic regression modeL Among postmenopausal-parous women, the risk of breast cancer was decreasing with increasing number of breastfed children(X²_(trend)=4.2(1) for hospital controls; X²_(trend)=1.2(l) for community controls). The trend of decreasing risk with increasing total months of breast feeding was also ob- served. Particularly noteworthy was a significant decreasing trend of breast cancer risk with months of breast feeding of the first child, regardless of parity and age at first full term pregnancy. This study confirmed an independent protective effect of breast feeding against breast cancer among postmenopausal women in Korea. These findings suggest that evasion of breast feeding may play a role in recent increase of breast cancer in Korea.
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.