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It is important to state what the event is and when the period of observation starts and finishes.For example, we may be interested in relapse in the time period between a confirmed response and the first relapse of cancer.
The specific difficulties relating to survival analysis arise largely from the fact that only some individuals have experienced the event and, subsequently, survival times will be unknown for a subset of the study group.
This phenomenon is called censoring and it may arise in the following ways: (a) a patient has not (yet) experienced the relevant outcome, such as relapse or death, by the time of the close of the study; (b) a patient is lost to follow-up during the study period; (c) a patient experiences a different event that makes further follow-up impossible.
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 the other plot, the data are presented in the format for a survival analysis where all-cause mortality is the event of interest.
Follow-up data were available up until the end of December 2000, by which time 550 (75.9%) had died (Clark et al, 2001).
Figure 1 shows data from 10 patients diagnosed in the early 1990s and illustrates how patient profiles in calendar time are converted to time to event (death) data.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.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.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.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).Event time data may also be interval censored, meaning that individuals come in and out of observation.If we consider the previous example and patients are also examined at 6 months, then those who are disease free at 3 months and lost to follow-up between 3 and 6 months are considered interval censored.