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Time-To-Event Data: An Overview and Analysis Considerations
被引:13
|作者:
Le-Rademacher, Jennifer
[1
]
Wang, Xiaofei
[2
]
机构:
[1] Mayo Clin, Div Clin Trials & Biostat, Rochester, MN 55902 USA
[2] Duke Univ, Dept Biostat & Bioinformat, Durham, NC USA
关键词:
Time-to-event data;
Survival analysis;
Competing risks;
Kaplan-Meier estimates;
Log-rank test;
Cox model;
HAZARDS;
THERAPY;
MODELS;
BIAS;
D O I:
10.1016/j.jtho.2021.04.004
中图分类号:
R73 [肿瘤学];
学科分类号:
100214 ;
摘要:
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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页码:1067 / 1074
页数:8
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