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Improved survival modeling in cancer research using a reduced piecewise exponential approach
Author(s) -
Han Gang,
Schell Michael J.,
Kim Jongphil
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5915
Subject(s) - piecewise , nonparametric statistics , survival analysis , proportional hazards model , exponential function , parametric statistics , exponential distribution , computer science , econometrics , statistics , hazard , parametric model , mathematics , mathematical analysis , chemistry , organic chemistry
Statistical models for survival data are typically nonparametric, for example, the Kaplan–Meier curve. Parametric survival modeling, such as exponential modeling, however, can reveal additional insights and be more efficient than nonparametric alternatives. A major constraint of the existing exponential models is the lack of flexibility due to distribution assumptions. A flexible and parsimonious piecewise exponential model is presented to best use the exponential models for arbitrary survival data. This model identifies shifts in the failure rate over time based on an exact likelihood ratio test, a backward elimination procedure, and an optional presumed order restriction on the hazard rate. Such modeling provides a descriptive tool in understanding the patient survival in addition to the Kaplan–Meier curve. This approach is compared with alternative survival models in simulation examples and illustrated in clinical studies. Copyright © 2013 John Wiley & Sons, Ltd.