TY - JOUR
T1 - Detecting multiple change points in piecewise constant hazard functions
AU - Goodman, Melody S.
AU - Li, Yi
AU - Tiwari, Ram C.
N1 - Funding Information:
The research of M.S.G. was supported by National Institute of Child Health and Human Development grant 5 F31 HD043695. The research of Y.L. was supported by National Institute of Health grant R01CA95747. Views expressed by R.C.T. here are his own and do not necessarily represent those of Food and Drug Administration.
PY - 2011/11
Y1 - 2011/11
N2 - 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.
AB - 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.
KW - Cancer
KW - Change points
KW - Hazard function
KW - Piecewise constant
KW - Survival analysis
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U2 - 10.1080/02664763.2011.559209
DO - 10.1080/02664763.2011.559209
M3 - Article
AN - SCOPUS:79960892854
SN - 0266-4763
VL - 38
SP - 2523
EP - 2532
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 11
ER -