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Detecting multiple change points in piecewise constant hazard functions

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  • Melody S. Goodman
  • Yi Li
  • Ram C. Tiwari

Abstract

The National Cancer Institute (NCI) suggests a sudden reduction in prostate cancer mortality rates, likely due to highly successful treatments and screening methods for early diagnosis. We are interested in understanding the impact of medical breakthroughs, treatments, or interventions, on the survival experience for a population. For this purpose, estimating the underlying hazard function, with possible time change points, would be of substantial interest, as it will provide a general picture of the survival trend and when this trend is disrupted. Increasing attention has been given to testing the assumption of a constant failure rate against a failure rate that changes at a single point in time. We expand the set of alternatives to allow for the consideration of multiple change-points, and propose a model selection algorithm using sequential testing for the piecewise constant hazard model. These methods are data driven and allow us to estimate not only the number of change points in the hazard function but where those changes occur. Such an analysis allows for better understanding of how changing medical practice affects the survival experience for a patient population. We test for change points in prostate cancer mortality rates using the NCI Surveillance, Epidemiology, and End Results dataset.

Suggested Citation

  • Melody S. Goodman & Yi Li & Ram C. Tiwari, 2011. "Detecting multiple change points in piecewise constant hazard functions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2523-2532, January.
  • Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2523-2532
    DOI: 10.1080/02664763.2011.559209
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    Cited by:

    1. Cai, Xia & Tian, Yubin & Ning, Wei, 2017. "Modified information approach for detecting change points in piecewise linear failure rate function," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 130-140.

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