For example, consider a study investigating the time to recurrence of a cancer following surgical removal of the primary tumour. Censoring can also occur if we observe the presence of a state or condition but do not know where it began. This situation is often called right censoring. Visualising the survival process of an individual as a time-line, their event (assuming it were to occur) is beyond the end of the follow-up period. Such censored survival times underestimate the true (but unknown) time to event.
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. 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. Several introductory texts also describe the basis of survival analysis, for example, Altman (2003) and Piantadosi (1997).ĬENSORING MAKES SURVIVAL ANALYSIS DIFFERENT In addition, individual references for the methods are presented throughout the series. 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).
#LOVE DEATH 4 UNCENSORED SERIES#
Future papers in the series cover multivariate analysis and the last paper introduces some more advanced concepts in a brief question and answer format.
#LOVE DEATH 4 UNCENSORED HOW TO#
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. 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. Most survival analyses in cancer journals use some or all of Kaplan–Meier (KM) plots, logrank tests, and Cox (proportional hazards) regression. This paper is the first of a series of four articles that aim to introduce and explain the basic concepts of survival analysis. It is these features of the data that make the special methods called survival analysis necessary. Further, survival data are rarely Normally distributed, but are skewed and comprise typically of many early events and relatively few late ones. 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.
If the event occurred in all individuals, many methods of analysis would be applicable. 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. In many cancer studies, the main outcome under assessment is the time to an event of interest.