預防醫學研究所;Institute of Preventive MedicineChen, Li-ShengLi-ShengChenYen, Ming-FangMing-FangYenWu, Hui-MinHui-MinWuLiao, Chao-ShengChao-ShengLiaoLiou, Der-MingDer-MingLiouKuo, Hsu-SungHsu-SungKuoChen, Tony Hsiu-HsiTony Hsiu-HsiChen陳秀熙2008-08-202018-06-292008-08-202018-06-292005http://ntur.lib.ntu.edu.tw//handle/246246/81433Aims and objectives: Computer program for the prediction of survival with respect to time-dependent proportional hazards regression model has been rarely addressed. We therefore developed a SAS Macro program for time-dependent Cox regression predictive model for empirical survival data associated with time-dependent covariates. Method: Time- dependent proportional hazards regression model and partial likelihood in association with time-varying predictors were explicitly delineated. Baseline hazard using Andersen's method was incorporated into proportional hazards regression model to predict the dynamic change of cumulative survival in respect of time-varying predictors. Two SAS Macro programs for time-dependent predictive survival model and model validation using receiver operative characteristics were written with SAS IML language. Results: The computer program was applied to data on clinical surveillance of small hepatocellular carcinoma (HCC) treated by percutaneous ethanol injection (PEI) or transcatheter arterial embolization (TAE) with time-varying predictors such as alpha-feto protein (AFP) and other biological markers. Conclusion: The program is very useful for real-time prediction of cumulative survival on the basis of time- dependent covariates.en-USSAS Macro programsmall hepatocellular carcinomasurvivaltime-dependent Cox regression modeljournal article