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Bayesian nonparametric modeling of heterogeneous time-to-event data with an unknown number of sub-populations

Author

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  • Mingyang Li
  • Jiali Han
  • Jian Liu

Abstract

Time-to-event data are a broad class of data widely encountered at different stages of the product life cycle. In practice, time-to-event data often exhibit heterogeneity, due to a variety of design and manufacturing issues, such as material quality inhomogeneity, unverified design changes, and manufacturing defects. Existing time-to-event modeling approaches mainly ignore this heterogeneity or account for it by pre-determining a fixed number of sub-populations. However, neglecting heterogeneity hinders the modeling accuracy, whereas pre-determining the number of sub-populations is often subjective or unjustifiable. In this article, a Bayesian nonparametric model is proposed to model heterogeneous time-to-event data by assuming an unknown number of sub-populations and quantifying the influence of possible covariates. An estimation algorithm is further proposed to achieve the joint model estimation and selection and to deal with the non-conjugate priors. Case studies demonstrate the effectiveness of the proposed work.

Suggested Citation

  • Mingyang Li & Jiali Han & Jian Liu, 2017. "Bayesian nonparametric modeling of heterogeneous time-to-event data with an unknown number of sub-populations," IISE Transactions, Taylor & Francis Journals, vol. 49(5), pages 481-492, May.
    • Handle: RePEc:taf:uiiexx:v:49:y:2017:i:5:p:481-492
    DOI: 10.1080/0740817X.2016.1234732
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    Cited by:

    1. Yang, Lechang & Wang, Pidong & Wang, Qiang & Bi, Sifeng & Peng, Rui & Behrensdorf, Jasper & Beer, Michael, 2021. "Reliability analysis of a complex system with hybrid structures and multi-level dependent life metrics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Li, Mingyang & Meng, Hongdao & Zhang, Qingpeng, 2017. "A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 95-104.

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