Survival analysis part I: basic concepts and first analyses.
Survival analysis part I: basic concepts and first analyses.
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DOI：
10.1038/sj.bjc.6601118
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发表时间：
2003-07-21
影响因子：
8.8
通讯作者：
中科院分区：
文献类型：
--
作者：
在许多癌症研究中，评估的主要结局是关注事件的时间。时间的通用名称是生存时间，尽管它可以应用于从完全缓解到复发或进展的“存活”时间，以及从诊断到死亡的时间。如果事件发生在所有人身上，那么许多分析方法都是适用的。然而，通常在随访结束时，一些个体尚未发生关注的事件，因此其发生事件的真实时间未知。此外，生存数据很少是正态分布的，而是偏斜的，通常包括许多早期事件和相对较少的晚期事件。正是数据的这些特征使得称为生存分析的特殊方法成为必要。本文是旨在介绍和解释生存分析基本概念的四篇系列文章中的第一篇。癌症期刊中的大多数生存分析使用Kaplan-Meier（KM）图、对数秩检验和考克斯（比例风险）回归的部分或全部。我们将讨论每种方法的背景和解释，以及其他值得更经常使用的分析方法。在第一篇文章中，我们将介绍生存分析的基本概念，包括如何产生和解释生存曲线，以及如何量化和测试两组或多组患者之间的生存差异。未来的论文在该系列涵盖多元分析和最后的论文介绍了一些更先进的概念，在一个简短的问题和答案的格式。关于这些方法的更详细的描述可以在专门关于生存分析的书籍中找到，例如Collett（1994），Parmar and Machin（1995）和Kleinbaum（1996）。此外，在整个系列中还提供了这些方法的个别参考文献。一些介绍性的文本也描述了生存分析的基础，例如Altman（2003）和Piantadosi（1997）。
In many cancer studies, the main outcome under assessment is the time to an event of interest. The generic name for the time is survival time, although it may be applied to the time ‘survived’from complete remission to relapse or progression as equally as to the time from diagnosis to death. If the event occurred in all individuals, many methods of analysis would be applicable. However, it is usual that at the end of follow-up some of the individuals have not had the event of interest, and thus their true time to event is unknown. Further, survival data are rarely Normally distributed, but are skewed and comprise typically of many early events and relatively few late ones. It is these features of the data that make the special methods called survival analysis necessary. This paper is the first of a series of four articles that aim to introduce and explain the basic concepts of survival analysis. Most survival analyses in cancer journals use some or all of Kaplan–Meier (KM) plots, logrank tests, and Cox (proportional hazards) regression. We will discuss the background to, and interpretation of, each of these methods but also other approaches to analysis that deserve to be used more often. In this first article, we will present the basic concepts of survival analysis, including how to produce and interpret survival curves, and how to quantify and test survival differences between two or more groups of patients. Future papers in the series cover multivariate analysis and the last paper introduces some more advanced concepts in a brief question and answer format. More detailed accounts of these methods can be found in books written specifically about survival analysis, for example, Collett (1994), Parmar and Machin (1995) and Kleinbaum (1996). In addition, individual references for the methods are presented throughout the series. Several introductory texts also describe the basis of survival analysis, for example, Altman (2003) and Piantadosi (1997).
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影响因子：
4.5
作者：
RAMLAUHANSEN, H
通讯作者：
RAMLAUHANSEN, H
影响因子：
8.8
作者：
Peto R;Pike MC;Armitage P;Breslow NE;Cox DR;Howard SV;Mantel N;McPherson K;Peto J;Smith PG
通讯作者：
Smith PG
影响因子：
168.9
作者：
Pocock, SJ;Clayton, TC;Altman, DG
通讯作者：
Altman, DG
影响因子：
3.7
作者：
KAPLAN, EL;MEIER, P
通讯作者：
MEIER, P
影响因子：
8.8
作者：
Clark TG;Stewart ME;Altman DG;Gabra H;Smyth JF
通讯作者：
Smyth JF