Survival Analysis
Survival analysis (time-to-event analysis) studies the time until an event of interest occurs, accounting for censored data.
1Detailed Explanation
Survival analysis handles patients who have not experienced the event by the end of follow-up ('censored' observations). Key methods include: Kaplan-Meier estimator for survival probabilities, log-rank test for comparing survival curves, Cox proportional hazards regression for adjusted hazard ratios, and parametric survival models. Key concepts include: event (outcome), time-to-event, censoring (right, left, interval), hazard, survival probability, median survival, and hazard ratio. Proportional hazards assumption should be checked. Competing risks and recurrent events require specialized methods. Time-dependent covariates andfrailty models address non-proportional hazards.
2Examples
- A.Kaplan-Meier curve showing median OS of 18 months in treatment group vs. 12 months in control
- B.Cox regression: HR = 0.65 (95% CI: 0.50-0.85), indicating 35% reduction in hazard with treatment
3Why It Matters in Research
Survival analysis is essential for oncology, cardiology, and any field where time to event is the primary outcome. Mastery of Cox regression is expected for medical researchers.
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