MKVPCI: a computer program for Markov models with piecewise constant intensities and covariates

We present a computer program for fitting Markov models with piecewise constant intensities and for estimating the effect of covariates on transition intensities. The basic idea of the proposed approach is to introduce artificial time-dependent covariates in the data to represent the time dependence of the transition intensities, and to use a modified time-homogeneous Markov model to estimate the baseline transition intensities and the regression coefficients. The program provides the maximum likelihood estimates of the parameters together with their estimated standard errors, and allows testing various statistical hypotheses. To illustrate the use of the program, we present a three-state model for analyzing the smoking habits of school children.

[1]  N Keiding,et al.  Confirmatory analysis of survival data using left truncation of the life times of primary survivors. , 1987, Statistics in medicine.

[2]  J. Griffiths The Theory of Stochastic Processes , 1967 .

[3]  J. Ward,et al.  Statistical analysis of the stages of HIV infection using a Markov model. , 1989, Statistics in medicine.

[4]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[5]  R H Jones,et al.  MARKOV: a computer program for multi-state Markov models with covariables. , 1995, Computer methods and programs in biomedicine.

[6]  R H Jones,et al.  Multi-state models and diabetic retinopathy. , 1995, Statistics in medicine.

[7]  J F Lawless,et al.  Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV disease. , 1994, Statistics in medicine.

[8]  D. Commenges,et al.  Effect of Gender, Age, Transmission Category, and Antiretroviral Therapy on the Progression of Human Immunodeficiency Virus Infection Using Multistate Markov Models , 1998, Epidemiology.

[9]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[10]  R Kay,et al.  A Markov model for analysing cancer markers and disease states in survival studies. , 1986, Biometrics.

[11]  J. Kalbfleisch,et al.  The Analysis of Panel Data under a Markov Assumption , 1985 .

[12]  P. Andersen,et al.  Multistate models in survival analysis: a study of nephropathy and mortality in diabetes. , 1988, Statistics in medicine.