Fitting a semi-parametric mixture model for competing risks in survival data

G Escarela, Russell Bowater

    Research output: Contribution to journalArticle

    6 Citations (Scopus)

    Abstract

    A model for survival analysis is studied that is relevant for samples which are subject to multiple types of failure. In comparison with a more standard approach, through the appropriate use of hazard functions and transition probabilities, the model allows for a more accurate study of cause-specific failure with regard to both the timing and type of failure. A semiparametric specification of a mixture model is employed that is able to adjust for concomitant variables and allows for the assessment of their effects on the probabilities of eventual causes of failure through a generalized logistic model, and their effects on the corresponding conditional hazard functions by employing the Cox proportional hazards model. A carefully formulated estimation procedure is presented that uses an EM algorithm based on a profile likelihood construction. The methods discussed, which could also be used for reliability analysis, are applied to a prostate cancer data set.
    Original languageEnglish
    Pages (from-to)277-293
    Number of pages17
    JournalCommunications in Statistics: Theory and Methods
    Volume37
    Issue number2
    DOIs
    Publication statusPublished - 1 Jan 2008

    Keywords

    • product-limit estimate
    • multiple decrement data
    • split-population models
    • EM algorithm
    • event-specific hazard

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