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2017 ; 62
(3
): 251-257
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Biostatistics Series Module 9: Survival Analysis
#MMPMID28584366
Hazra A
; Gogtay N
Indian J Dermatol
2017[May]; 62
(3
): 251-257
PMID28584366
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Survival analysis is concerned with "time to event" data. Conventionally, it
dealt with cancer death as the event in question, but it can handle any event
occurring over a time frame, and this need not be always adverse in nature. When
the outcome of a study is the time to an event, it is often not possible to wait
until the event in question has happened to all the subjects, for example, until
all are dead. In addition, subjects may leave the study prematurely. Such
situations lead to what is called censored observations as complete information
is not available for these subjects. The data set is thus an assemblage of times
to the event in question and times after which no more information on the
individual is available. Survival analysis methods are the only techniques
capable of handling censored observations without treating them as missing data.
They also make no assumption regarding normal distribution of time to event data.
Descriptive methods for exploring survival times in a sample include life table
and Kaplan-Meier techniques as well as various kinds of distribution fitting as
advanced modeling techniques. The Kaplan-Meier cumulative survival probability
over time plot has become the signature plot for biomedical survival analysis.
Several techniques are available for comparing the survival experience in two or
more groups - the log-rank test is popularly used. This test can also be used to
produce an odds ratio as an estimate of risk of the event in the test group; this
is called hazard ratio (HR). Limitations of the traditional log-rank test have
led to various modifications and enhancements. Finally, survival analysis offers
different regression models for estimating the impact of multiple predictors on
survival. Cox's proportional hazard model is the most general of the regression
methods that allows the hazard function to be modeled on a set of explanatory
variables without making restrictive assumptions concerning the nature or shape
of the underlying survival distribution. It can accommodate any number of
covariates, whether they are categorical or continuous. Like the adjusted odds
ratios in logistic regression, this multivariate technique produces adjusted HRs
for individual factors that may modify survival.
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