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Printable Handouts
Navigable Slide Index
- Introduction
- Outline
- Survival analysis
- What is survival analysis?
- Types of studies
- Probability functions
- Survival function, median/mean survival time
- Hazard function and cumulative hazard function
- Relationship of probability functions
- Censoring
- Right censoring
- Interval censoring
- Left censoring
- Truncation
- Estimators of survival function
- Kaplan Meier estimator
- Kaplan Meier example
- Nelson-Aalen estimator
- Log rank test
- Cox regression model
- Components of a cox regression model
- Proportional hazards assumption
- Test for proportional hazards assumption
- Diagnostics for cox model
- Time-dependent covariates
- Competing risk
- Statistical methods for competing risk
- Selection bias in randomized clinical trials
- Frailty models
- Types of frailty models
- Statistical software
Topics Covered
- Censoring
- Truncation
- Survival probability
- Failure probability
- Probability Density Function
- Hazard function
- Cumulative Hazard Function
- Kaplan Meier
- Semi-parametric
- Nelson-Aalen Estimator
- Log rank test
- Selection Bias
- Frailty models
- Competing risk
Links
Series:
Categories:
Talk Citation
Odia, I. (2016, October 31). Survival analysis [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved October 12, 2024, from https://doi.org/10.69645/IXDQ4879.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Isoken Odia has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Other Talks in the Series: The Risk of Bias in Randomized Clinical Trials
Transcript
Please wait while the transcript is being prepared...
0:00
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.
0:12
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.
0:40
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.
1:33
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
certain groups.
We want to estimate
the timing to the event.
We want to compare the time to event for
different groups.
We want to assess how setting
variables affects the time to event.