Cluster validity indices for mixture hazards regression models

Yi Wen Chang, Kang Ping Lu, Shao Tung Chang

研究成果: 雜誌貢獻文章

摘要

In the analysis of survival data, the problems of competing risks arise frequently in medical applications where individuals fail from multiple causes. Semiparametric mixture regression models have become a prominent approach in competing risks analysis due to their flexibility and easy interpretation of resultant estimates. The literature presents several semiparametric methods on the estimations for mixture Cox proportional hazards models, but fewer works appear on the determination of the number of model components and the estimation of baseline hazard functions using kernel approaches. These two issues are important because both incorrect number of components and inappropriate baseline functions can lead to insufficient estimates of mixture Cox hazard models. This research thus proposes four validity indices to select the optimal number of model components based on the posterior probabilities and residuals resulting from the application of an EM-based algorithm on a mixture Cox regression model. We also introduce a kernel approach to produce a smooth estimate of the baseline hazard function in a mixture model. The effectiveness and the preference of the proposed cluster indices are demonstrated through a simulation study. An analysis on a prostate cancer dataset illustrates the practical use of the proposed method.

原文英語
頁(從 - 到)1616-1636
頁數21
期刊Mathematical Biosciences and Engineering
17
發行號2
DOIs
出版狀態已發佈 - 2020 一月 1

ASJC Scopus subject areas

  • Modelling and Simulation
  • Agricultural and Biological Sciences(all)
  • Computational Mathematics
  • Applied Mathematics

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