Abstract
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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