title: SAS macro program for non-homogeneous Markov process in modeling multi-state disease progression.
abstract: 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.
authors.affiliation: College of Public Health, Institute of Epidemiology, National Taiwan University, Taipei, Taiwan.
authors.forename: Wu
authors.initials: W
authors.lastname: Hui-Min
authors.forename: Yen
authors.initials: Y
authors.lastname: Ming-Fang
authors.forename: Tony Hsiu-Hsi
authors.initials: TH
authors.lastname: Chen
mesh_terms: D002908:Chronic Disease; D018450:Disease Progression; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D009369:Neoplasms / Q000473:pathology