Hui-Min Wu, Ming-Fang Yen, Chen Tony Hsiu-Hsi
College of Public Health, Institute of Epidemiology, National Taiwan University, Taipei, Taiwan.
Comput Methods Programs Biomed. 2004 Aug;75(2):95-105. doi: 10.1016/j.cmpb.2003.12.001.
Writing 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 colorectal 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.
编写一个用于模拟癌症或慢性病多状态疾病进程的计算机程序通常是一项艰巨且耗时的任务。我们开发了一个SAS宏程序，用于使用SAS IML估计此类模型中的转移参数。该程序非常灵活，用户可以指定齐次和非齐次（即威布尔分布、对数逻辑斯蒂等）马尔可夫模型，使用比例风险形式纳入协变量，推导转移概率，构建似然函数，并在SAS子例程中计算最大似然估计（MLE）和95%置信区间。该程序已成功应用于一个三状态疾病模型的示例，该模型用于描述结直肠癌从正常（无疾病）到腺瘤（癌前疾病），最终到浸润性癌的进展过程，无论是否对协变量进行调整。这个宏程序可以推广到具有s个协变量的其他k状态模型。
