SAS macro program for non-homogeneous Markov process in modeling multi-state disease progression

Wu Hui-Min, Yen Ming-Fang, Tony Hsiu-Hsi Chen

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)95-105
Number of pages11
JournalComputer Methods and Programs in Biomedicine
Issue number2
Publication statusPublished - Aug 2004
Externally publishedYes


  • Exponential regression model
  • Markov model
  • Multi-state model

ASJC Scopus subject areas

  • Software


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