Testing dependence between the failure time and failure modes: an application of enlarged filtration

Dario Gasbarra, Sangita Kulathinal, Isha Dewan, Aulikki Nissinen

    Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

    Sammanfattning

    The model of independent competing risks provides no information for the assessment of competing failure modes if the failure mechanisms underlying these modes are coupled. Models for dependent competing risks in the literature can be distinguished on the basis of the functional behaviour of the conditional probability of failure due to a particular failure mode given that the failure time exceeds a fixed time, as a function of time. There is an interesting link between monotonicity of such conditional probability and dependence between failure time and failure mode, via crude hazard rates. In this paper, we propose tests for testing the dependence between failure time and failure mode using the crude hazards and using the conditional probabilities mentioned above. We establish the equivalence between the two approaches and provide an asymptotically efficient weight function under a sequence of local alternatives. The tests are applied to simulated data and to mortality follow-up data. (c) 2005 Elsevier B.V. All rights reserved.
    Originalspråkengelska
    TidskriftJournal of Statistical Planning and Inference
    Volym136
    Sidor (från-till)1669-1686
    Antal sidor18
    ISSN0378-3758
    DOI
    StatusPublicerad - 2006
    MoE-publikationstypA1 Tidskriftsartikel-refererad

    Vetenskapsgrenar

    • 111 Matematik

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    abstract = "The model of independent competing risks provides no information for the assessment of competing failure modes if the failure mechanisms underlying these modes are coupled. Models for dependent competing risks in the literature can be distinguished on the basis of the functional behaviour of the conditional probability of failure due to a particular failure mode given that the failure time exceeds a fixed time, as a function of time. There is an interesting link between monotonicity of such conditional probability and dependence between failure time and failure mode, via crude hazard rates. In this paper, we propose tests for testing the dependence between failure time and failure mode using the crude hazards and using the conditional probabilities mentioned above. We establish the equivalence between the two approaches and provide an asymptotically efficient weight function under a sequence of local alternatives. The tests are applied to simulated data and to mortality follow-up data. (c) 2005 Elsevier B.V. All rights reserved.",
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    Testing dependence between the failure time and failure modes: an application of enlarged filtration. / Gasbarra, Dario; Kulathinal, Sangita; Dewan, Isha; Nissinen, Aulikki.

    I: Journal of Statistical Planning and Inference, Vol. 136, 2006, s. 1669-1686.

    Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

    TY - JOUR

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    AU - Kulathinal, Sangita

    AU - Dewan, Isha

    AU - Nissinen, Aulikki

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    AB - The model of independent competing risks provides no information for the assessment of competing failure modes if the failure mechanisms underlying these modes are coupled. Models for dependent competing risks in the literature can be distinguished on the basis of the functional behaviour of the conditional probability of failure due to a particular failure mode given that the failure time exceeds a fixed time, as a function of time. There is an interesting link between monotonicity of such conditional probability and dependence between failure time and failure mode, via crude hazard rates. In this paper, we propose tests for testing the dependence between failure time and failure mode using the crude hazards and using the conditional probabilities mentioned above. We establish the equivalence between the two approaches and provide an asymptotically efficient weight function under a sequence of local alternatives. The tests are applied to simulated data and to mortality follow-up data. (c) 2005 Elsevier B.V. All rights reserved.

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