Mixture multi-state Markov regression model

Amy Ming-Fang Yen, Tony Hsiu-Hsi Chen

研究成果: 雜誌貢獻文章同行評審

5 引文 斯高帕斯(Scopus)


Although heterogeneity across individuals may be reduced when a two-state process is extended into a multi-state process, the discrepancy between the observed and the predicted for some states may still exist owing to two possibilities, unobserved mixture distribution in the initial state and the effect of measured covariates on subsequent multi-state disease progression. In the present study, we developed a mixture Markov exponential regression model to take account of the above-mentioned heterogeneity across individuals (subject-to-subject variability) with a systematic model selection based on the likelihood ratio test. The model was successfully demonstrated by an empirical example on surveillance of patients with small hepatocellular carcinoma treated by non-surgical methods. The estimated results suggested that the model with the incorporation of unobserved mixture distribution behaves better than the one without. Complete and partial effects regarding risk factors on different subsequent multi-state transitions were identified using a homogeneous Markov model. The combination of both initial mixture distribution and homogeneous Markov exponential regression model makes a significant contribution to reducing heterogeneity across individuals and over time for disease progression.
頁(從 - 到)11-21
期刊Journal of Applied Statistics
出版狀態已發佈 - 1月 2007

ASJC Scopus subject areas

  • 統計與概率


深入研究「Mixture multi-state Markov regression model」主題。共同形成了獨特的指紋。