Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota.
Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.
J Thorac Oncol. 2021 Jul;16(7):1067-1074. doi: 10.1016/j.jtho.2021.04.004. Epub 2021 Apr 19.
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.
在肿瘤学中,总生存期和无进展生存期是常用的时间事件终点,用于衡量治疗效果。此类数据的分析依赖于复杂的统计框架,并且只有在数据满足某些假设时,分析结果才有效。本文概述了时间事件数据、常用分析方法的基本原理以及分析此类数据时经常遇到的问题。我们的目标是为临床医生和其他肺癌研究人员提供选择适当时间事件分析方法的知识,并适当解释此类分析的结果。我们强烈鼓励研究人员在开展包含时间事件数据的研究时寻求具有生存分析专业知识的统计学家,以确保他们的数据使用适当的方法进行收集和分析。