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.
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References in zbMATH (referenced in 4 articles )
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- Commenges, Daniel; Jacqmin-Gadda, Hélène: Dynamical biostatistical models (2016)
- Pan, Shin-Liang; Chen, Hsiu-Hsi: Time-varying Markov regression random-effect model with Bayesian estimation procedures: Application to dynamics of functional recovery in patients with stroke (2010)
- Commenges, D.: Inference for multi-state models from interval-censored data (2002)
- Alioum, Ahmadou; Commenges, Daniel: MKVPCI: a computer program for Markov models with piecewise constant intensities and covariates. (2001) ioport