title: Survival analysis of time-to-event data in respiratory health research studies.
abstract: This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan-Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia.
authors.forename: Jessica
authors.initials: J
authors.lastname: Kasza
authors.forename: Darren
authors.initials: D
authors.lastname: Wraith
authors.forename: Karen
authors.initials: K
authors.lastname: Lamb
authors.forename: Rory
authors.initials: R
authors.lastname: Wolfe
mesh_terms: D001315:Australia; D035843:Biomedical Research / Q000379:methods; D002986:Clinical Trials as Topic / Q000145:classification / Q000379:methods; D006801:Humans; D053208:Kaplan-Meier Estimate; D017567:Karnofsky Performance Status; D008175:Lung Neoplasms / Q000175:diagnosis / Q000401:mortality / Q000523:psychology / Q000628:therapy; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000639:trends; D016016:Proportional Hazards Models; D011788:Quality of Life; D013997:Time Factors; D016896:Treatment Outcome
keywords: Kaplan-Meier estimate; accelerated failure time model; proportional hazards model; survival analysis