title: Improved survival modeling in cancer research using a reduced piecewise exponential approach.
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.
authors.affiliation: Department of Biostatistics, Yale University School of Public Health, 60 College Street, New Haven, CT 06520, U.S.A.
authors.forename: Gang
authors.initials: G
authors.lastname: Han
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Schell
authors.forename: Jongphil
authors.initials: J
authors.lastname: Kim
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009369:Neoplasms / Q000401:mortality*; D016019:Survival Analysis
keywords: exponential survival; median survival; nonsmall cell lung cancer; survival analysis
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