%0 Journal Article %1 Chen2005 %A Chen, Li-Sheng %A Yen, Ming-Fang %A Wu, Hui-Min %A Liao, Chao-Sheng %A Liou, Der-Ming %A Kuo, Hsu-Sung %A Chen, Tony Hsiu-Hsi %D 2005 %J Journal of evaluation in clinical practice %K Carcinoma Computer-Assisted Diagnosis Hepatocellular Hepatocellular:diagnosis Hepatocellular:drugtherapy Hepatocellular:mortality Humans LiverNeoplasms LiverNeoplasms:diagnosis LiverNeoplasms:drugtherapy LiverNeoplasms:mortality Prognosis ProportionalHazardsModels ROCCurve SurvivalAnalysis TimeFactors %N 2 %P 181-93 %R 10.1111/j.1365-2753.2005.00519.x %T Predictive survival model with time-dependent prognostic factors: development of computer-aided SAS Macro program. %U http://www.ncbi.nlm.nih.gov/pubmed/15813715 %V 11 %X AIMS 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.

@article{Chen2005, abstract = {AIMS 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.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Chen, Li-Sheng and Yen, Ming-Fang and Wu, Hui-Min and Liao, Chao-Sheng and Liou, Der-Ming and Kuo, Hsu-Sung and Chen, Tony Hsiu-Hsi}, biburl = {https://www.bibsonomy.org/bibtex/24acc87f4d149cc0cb92eacecd48564bc/jepcastel}, doi = {10.1111/j.1365-2753.2005.00519.x}, interhash = {b6fd347c862b38a3d289e67cf5f6c15f}, intrahash = {4acc87f4d149cc0cb92eacecd48564bc}, issn = {1356-1294}, journal = {Journal of evaluation in clinical practice}, keywords = {Carcinoma Computer-Assisted Diagnosis Hepatocellular Hepatocellular:diagnosis Hepatocellular:drugtherapy Hepatocellular:mortality Humans LiverNeoplasms LiverNeoplasms:diagnosis LiverNeoplasms:drugtherapy LiverNeoplasms:mortality Prognosis ProportionalHazardsModels ROCCurve SurvivalAnalysis TimeFactors}, month = {4}, note = {3816<m:linebreak></m:linebreak>Anàlisi de supervivència; SAS}, number = 2, pages = {181-93}, pmid = {15813715}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Predictive survival model with time-dependent prognostic factors: development of computer-aided SAS Macro program.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15813715}, volume = 11, year = 2005 }