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• W2962783943 abstract "In this article, we suggest a new statistical approach considering survival heterogeneity as a breakpoint model in an ordered sequence of time-to-event variables. The survival responses need to be ordered according to a numerical covariate. Our estimation method will aim at detecting heterogeneity that could arise through the ordering covariate. We formally introduce our model as a constrained Hidden Markov Model, where the hidden states are the unknown segmentation (breakpoint locations) and the observed states are the survival responses. We derive an efficient Expectation-Maximization framework for maximizing the likelihood of this model for a wide range of baseline hazard forms (parametrics or nonparametric). The posterior distribution of the breakpoints is also derived, and the selection of the number of segments using penalized likelihood criterion is discussed. The performance of our survival breakpoint model is finally illustrated on a diabetes dataset where the observed survival times are ordered according to the calendar time of disease onset." @default.
• W2962783943 created "2019-07-30" @default.
• W2962783943 creator A5038882011 @default.
• W2962783943 creator A5087944743 @default.
• W2962783943 date "2017-05-01" @default.
• W2962783943 modified "2023-10-18" @default.
• W2962783943 title "A change-point model for detecting heterogeneity in ordered survival responses" @default.
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• W2962783943 doi "https://doi.org/10.1177/0962280217707231" @default.
• W2962783943 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28459175" @default.
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