Improved Survival Modeling in Cancer Research Using a Reduced Piecewise Exponential Approach
Document Type
Article
Publication Date
2014
Keywords
survival analysis, exponential survival, nonsmall cell lung cancer, median survival
Abstract
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.
Digital Object Identifier (DOI)
https://doi.org/10.1002/sim.5915
Citation / Publisher Attribution
Statistics in Medicine, v. 33, issue 1, p. 59-73
Scholar Commons Citation
Han, Gang; Schell, Michael J.; and Kim, Jongphil, "Improved Survival Modeling in Cancer Research Using a Reduced Piecewise Exponential Approach" (2014). Oncologic Sciences Faculty Publications. 22.
https://digitalcommons.usf.edu/onc_facpub/22
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