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113.020.020.30 Survival analysis - Pros and cons
Survival Analysis - Pros and Cons
Bottom-line:
- Kaplan-Meier is simple, but overly simple and doesn't work well for estimation b/c it is non-parametric and you can't easily estimate numbers that in-between times.
- Weibull is an exponential model which has a hazard that can increase proportionally as time (t) progresses, which is not always realistic
- Proportional Hazards is most accurate b/c the Hazard can fluctuate as Time goes on, but it's also the most complicated and you can't estimate the survival function.
Models
Kaplan Meier
- Pros
- Simple
- You can estimate the survival
- Cons
- No functional form - no simple mathematical function that describes it
- Can not estimate the hazard ratio
- Can only include a few categorical
x
's
Exponential
- Pros
- Can estimate
S(t)
and HR
- Can estimate
- Cons
- Not always realistic
- Weibull (parametric) allows HAZ to increase/decrease proportionally with time, but this isn't realistic all the time either
- Assumes a constant hazard, which is bad. E.g.
- as humans our risk of dying increases as we get older
- as babies we have a low risk, then higher risk as teens (risky behavior), then lower again at 30-40, then higher again as we go >60-70
- Not always realistic
Cox Proportional Hazard
- 113.020.020.50 Survival Analysis - Proportional Hazards PH
- Pros
- Hazard can fluctuate with time, can increase/decrease as time goes by
- Can estimate the hazard ratio
- Cons
- Can not estimate the survival function
S(t)
- Can not estimate the survival function
Source:
- Me
Graph:
- 113.020.020 Statistics - Survival Analysis >> 113.020.020.30 Survival analysis - Pros and cons
- 113.020.020.30 Survival analysis - Pros and cons >> 113.020.020.50 Survival Analysis - Proportional Hazards PH
- 113.020.020.30 Survival analysis - Pros and cons >> 113.020.020 Statistics - Survival Analysis