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Figure 1 (left) shows that four patients had a nonfatal relapse, one was lost to follow-up, and seven patients died (five from ovarian cancer).
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).
If we were interested solely in ovarian cancer deaths, then patients 5 and 6 – those who died from nonovarian causes – would be censored.
In general, it is good practice to choose an end-point that cannot be misclassified.
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.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.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).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.Figure 1 (right) is specific to the outcome or event of interest.Here, death from any cause, often called overall survival, was the outcome of interest.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.