預防醫學研究所;Institute of Preventive MedicineYEN, AMY, MING-FANGAMY, MING-FANGYENCHEN, TONY, HSIU-HISTONY, HSIU-HISCHEN陳秀熙2008-08-292018-06-292008-08-292018-06-292004http://ntur.lib.ntu.edu.tw//handle/246246/81680Writing a computer program for modeling multi-state disease process for cancer or chronic disease is often an arduous and time-consuming task. We have developed a SAS macro program for estimating the transition parameters in such models using SAS IML. The program is very flexible and enables the user to specify homogeneous and non-homogeneous( i.e. Weibull distribution, log-logistic, etc.) Markov models, incorporate covariates using the proportional hazards form, derive transition probabilities, formulate the likelihood function, and calculate the maximum likelihood estimate (MLE) and 95% confidence interval within a SAS subroutine. The program was successfully applied to an example of a three- state disease model for the progression of cotorectal cancer from normal (disease free), to adenoma( pre-invasive disease), and finally to invasive carcinoma, with or without adjusting for covariates. This macro program can be generalized to other k-state models with s covariates. (C) 2004 Published by Elsevier Ireland Ltd.en-USmulti-state modelMarkov modelexponential regression model[SDGs]SDG3journal article