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1 Welcome to #researchmethodologies with @DrAoifeBee
Kaplan-Meier (KM) curves are a wonderfully informative way of presenting survival outcomes over time. But how do we interpret them? Survival analysis determines the probability of a binary outcome (aka an event or a failure)
Kaplan-Meier (KM) curves are a wonderfully informative way of presenting survival outcomes over time. But how do we interpret them? Survival analysis determines the probability of a binary outcome (aka an event or a failure)
2/15
Survival means the event being studied has not occurred yet - the patient is still alive if you’re analysing mortality, the baby has not been delivered if analysing births, the patient has not yet met whatever criteria you have decided constitutes an event in your study.
Survival means the event being studied has not occurred yet - the patient is still alive if you’re analysing mortality, the baby has not been delivered if analysing births, the patient has not yet met whatever criteria you have decided constitutes an event in your study.
3/15
In medical research, the binary outcome of interest is commonly survival vs death though other outcomes/events can be used. KM curves are a visual way of showing the fraction of patients living over time after a treatment, or lack of treatment if in a control group.
In medical research, the binary outcome of interest is commonly survival vs death though other outcomes/events can be used. KM curves are a visual way of showing the fraction of patients living over time after a treatment, or lack of treatment if in a control group.
