Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 75, Issue 2, Pages 95-105Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2003.12.001
Keywords
multi-state model; Markov model; exponential regression model
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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 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.
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