In oncology, overall survival and progression-free survival are common time-to-event end points used to measure treatment efficacy. Analyses of this type of data rely on a complex statistical framework and the analysis results are only valid when the data meet certain assumptions. This article provides an overview of time-to-event data, the basic mechanics of common analysis methods, and issues often encountered when analyzing such data. Our goal is to provide clinicians and other lung cancer researchers with the knowledge to choose the appropriate time-to-event analysis methods and to interpret the outcomes of such analyses appropriately. We strongly encourage investigators to seek out statisticians with expertise in survival analysis when embarking on studies that include time-to-event data to ensure that their data are collected and analyzed using the appropriate methods. (c) 2021 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.
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Duke Univ, Dept Stat Sci, Durham, NC 27708 USADuke Univ, Dept Stat Sci, Durham, NC 27708 USA
Liu, Bo
Wruck, Lisa
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Duke Univ, Sch Med, Dept Biostat & Bioinformat, Durham, NC 27708 USA
Duke Clin Res Inst, Durham, NC 27701 USADuke Univ, Dept Stat Sci, Durham, NC 27708 USA
Wruck, Lisa
Li, Fan
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Duke Univ, Dept Stat Sci, Durham, NC 27708 USADuke Univ, Dept Stat Sci, Durham, NC 27708 USA
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Univ Cambridge, MRC Biostat Unit, Cambridge, EnglandUniv Cambridge, MRC Biostat Unit, Cambridge, England
Wang, Chunyu
Shen, Jiaming
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Univ Manchester, Dept Math, Manchester, EnglandUniv Cambridge, MRC Biostat Unit, Cambridge, England
Shen, Jiaming
Charalambous, Christiana
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Univ Manchester, Dept Math, Manchester, EnglandUniv Cambridge, MRC Biostat Unit, Cambridge, England
Charalambous, Christiana
Pan, Jianxin
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Beijing Normal Univ, BNU HKBU United Int Coll, Res Ctr Math, Beijing, Peoples R China
BNU HKBU United Int Coll, Guangdong Prov Key Lab Interdisciplinary Res & App, Beijing, Peoples R ChinaUniv Cambridge, MRC Biostat Unit, Cambridge, England