title: Cure models for the analysis of time-to-event data in cancer studies.
abstract: In settings when it is biologically plausible that some patients are cured after definitive treatment, cure models present an alternative to conventional survival analysis. Cure models can inform on the group of patients cured, by estimating the probability of cure, and identifying factors that influence it; while simultaneously focusing on time to recurrence and associated factors for the remaining patients.
authors.affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York.
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Jia
authors.forename: Camelia S
authors.initials: CS
authors.lastname: Sima
authors.forename: Murray F
authors.initials: MF
authors.lastname: Brennan
authors.forename: Katherine S
authors.initials: KS
authors.lastname: Panageas
mesh_terms: D018450:Disease Progression; D018572:Disease-Free Survival; D006801:Humans; D053208:Kaplan-Meier Estimate; D009369:Neoplasms / Q000401:mortality / Q000628:therapy; D016016:Proportional Hazards Models; D012107:Research Design; D018401:Sample Size; D012509:Sarcoma / Q000401:mortality / Q000628:therapy; D013997:Time Factors; D016896:Treatment Outcome
keywords: cure models; soft tissue sarcoma; survival analysis