Calculator Input
Formula Used
Kaplan-Meier overall survival:
S(t) = Π (1 - di / ni)
Here, di is the number of deaths in interval i. ni is the number at risk at the start of that interval.
Greenwood standard error:
SE{S(t)} = S(t) × √Σ[di / (ni(ni - di))]
Confidence interval:
CI = S(t) ± z × SE
Median survival:
The median survival is the first time where the Kaplan-Meier survival estimate drops to 0.50 or below.
Approximate hazard:
λ = -ln(S(t)) / t
Approximate exponential median:
Median ≈ ln(2) / λ
This hazard and median extension is an optional summary approximation. The Kaplan-Meier estimate remains the main result.
How to Use This Calculator
- Enter a study label for your cohort.
- Enter the initial number of patients at baseline.
- Select the confidence level you want to report.
- Add cumulative interval times in months.
- Enter deaths recorded in each interval.
- Enter censored observations for each interval.
- Press the calculate button.
- Review the summary metrics and Kaplan-Meier interval table.
- Use the CSV or PDF buttons to export the current result.
This calculator works best with grouped study data. For patient-level research, use a full survival analysis workflow.
Example Data Table
| Time (months) | At Risk | Deaths | Censored | KM Survival |
|---|---|---|---|---|
| 6 | 120 | 5 | 2 | 0.9583 |
| 12 | 113 | 8 | 3 | 0.8905 |
| 18 | 102 | 10 | 4 | 0.8032 |
| 24 | 88 | 9 | 5 | 0.7211 |
This example shows how survival changes as deaths and censoring accumulate over time.
About Overall Survival Analysis
Why overall survival matters
Overall survival is one of the clearest endpoints in statistics and clinical research. It measures the share of a cohort still alive at defined time points. Analysts use it to compare treatments, monitor real-world outcomes, and summarize follow-up performance. A structured calculator saves time and reduces reporting errors.
How this calculator works
This page uses interval-based Kaplan-Meier logic. You enter the initial cohort, then add deaths and censored cases at increasing time points. The calculator updates the at-risk population step by step. It then multiplies each conditional survival value to produce cumulative overall survival. This creates a practical estimate from grouped data.
Why censoring matters
Censored observations are not the same as deaths. A censored patient leaves observation or has incomplete follow-up before the event is recorded. Survival methods keep those patients in the risk set until their censoring time. That is why Kaplan-Meier analysis is more useful than a simple alive-versus-dead percentage.
What the result means
The main result is the final overall survival percentage at the last interval. The table also shows the survival path across all intervals. Confidence limits add useful uncertainty bounds. The standard error comes from Greenwood’s formula. Median survival is reported once the curve falls to fifty percent or lower.
When to use grouped survival estimates
This type of calculator is helpful for study summaries, dashboards, teaching, and rapid protocol reviews. It works well when you have interval counts but not full patient-level records. It is also useful for preparing presentations, internal notes, and plain-language summaries for teams.
Important interpretation note
Grouped results are informative, but they are still a simplified view. Formal studies may need patient-level Kaplan-Meier curves, log-rank tests, or Cox models. Use this tool for fast reporting and careful comparison. Use full survival software when your analysis needs deeper inference.
Frequently Asked Questions
1. What does overall survival mean?
Overall survival is the proportion of a study cohort still alive at a defined time point. It is commonly used in clinical studies and outcome reporting.
2. Why are censored cases included?
Censored cases contribute follow-up time before they leave observation. They are not counted as deaths. Kaplan-Meier methods handle them separately to avoid bias.
3. Does this calculator use Kaplan-Meier logic?
Yes. It applies interval-based Kaplan-Meier steps using deaths and censored counts entered for each time point. That makes it suitable for grouped survival summaries.
4. What is the confidence interval showing?
The confidence interval shows a reasonable range around the estimated survival value. It reflects sampling uncertainty and is derived here from Greenwood standard error.
5. When is median survival reached?
Median survival is reached at the first time where the survival estimate drops to fifty percent or lower. If that never happens, the median is not reached.
6. Can I use this for patient-level research?
You can use it for quick summaries, but patient-level analysis needs dedicated survival software. Full studies may also require curve plots, stratification, and regression models.
7. What does approximate hazard mean here?
It is a simple summary derived from the final survival estimate and final time point. It gives a rough constant-rate interpretation, not a full hazard model.
8. Why export to CSV or PDF?
CSV is useful for spreadsheets and audits. PDF is useful for sharing reports, meeting notes, and study summaries in a compact format.