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Hi, my name is Isoken Odia and
I'm a biostatistician for
the Jaeb Center for Health Research.
I'm going to be giving a talk on
the key concepts of survival analysis.
The outline for
this presentation includes,
first I'm going to talk on
what survival analysis is.
I'm going to discuss probability
functions, censoring and truncation,
estimators of survival function,
the Cox regression model, and
the proportional hazards assumption,
competing risk, selection bias
in randomized clinical trials,
frailty models and statistical software.
First, I want to use an illustration to
describe what survival analysis means.
During a soccer game there's a draw,
and we need a winner.
Usually you have to go for a penalty kick.
There are two teams, and each person
has to play the ball into the net.
They keep on playing the ball
until someone loses.
In survival analysis it's a bit similar.
We keep on following the participants
till they have a failure,
usually it's called 'the event'.
So if Beckham shoots the first goal,
we keep on following everybody
until someone loses the ball.
That's kind of what
survival analysis looks at.
We are looking at the timing to failure,
timing to losing the game,
timing to lose the ball.
So what is survival analysis?
Survival analysis is
a statistical method for
analyzing survival data,
which is longitudinal time to event data.
In this situation, our outcome of
interest is the time to an event.
An event is usually the transition
from one state to the other.
It may be disease relapse,
it could be death,
it could be disease remission- just
moving from one state to the other.
Initially survival analysis was
termed survival because they were
always looking at the time to death.
So how many people survived
past the particular time?
Objectives of survival analysis:
What do we want to look at?
We want to look at the time to event for
We want to estimate
the timing to the event.
We want to compare the time to event for
We want to assess how setting
variables affects the time to event.