Kaplan Meier Survival Analysis
The Kaplan-Meier calculator estimates survival probability over time from time-to-event data, with proper handling of censored observations. Supports four analysis modes from simple single-cohort curves to propensity-score-matched comparisons.
Step 1 — Choose analysis type
| Type | Use case |
|---|---|
| Single Group Analysis | Descriptive survival in one cohort |
| Two Group Comparison | Treatment vs control with log-rank test |
| Multiple Group Comparison | Multi-arm trials with pairwise comparisons |
| Propensity Score Matching | Observational studies with confounding |
Step 2 — Provide your data
Each subject needs:
- Time — follow-up time
- Event — 1 (event occurred) or 0 (censored)
- Group — group label (for multi-group modes)
- Covariates — for propensity matching
Supports Excel Import, Manual Entry, and Sample Data Generator.
Step 3 — Results
Always shown:
- Survival points table — time, n at risk, events, censored, S(t), SE, 95% CI
- Median Survival Time with 95% CI
- Mean Survival Time with 95% CI
- Kaplan-Meier Curve with censored marks
For group comparisons:
- Log-rank test — chi-square, df, p-value
- Hazard Ratio with 95% CI
- Pairwise comparisons (multi-group)
For propensity matching:
- Propensity scores and matching results
- Before/after balance assessment
- Matched-pair analysis
A UniversalChatBot is available for discussion.
Statistical methods used
Kaplan-Meier product-limit estimator
S(t_i) = S(t_{i-1}) * (1 - d_i / n_i)
Standard error — Greenwood's formula
SE(S(t)) = S(t) * sqrt(sum(d_i / (n_i * (n_i - d_i))))
Median survival — smallest t where S(t) <= 0.5, with linear interpolation.
Log-rank test
For each event time: observed vs expected events per group.
chi2 = (O - E)^2 / V with df = k-1.
Hazard ratio from log-rank
HR = exp((O - E) / V) with 95% CI.
Propensity Score Matching — nearest-neighbour 1:1 matching on logistic regression propensity scores.