Turn raw time records into survival probability steps. Review censoring effects, failures, and remaining risk. Export clean outputs for audits, teaching, research, and reporting.
| time | event |
|---|---|
| 2 | 1 |
| 3 | 0 |
| 4 | 1 |
| 4 | 1 |
| 5 | 0 |
| 6 | 1 |
| 7 | 0 |
| 8 | 1 |
| 10 | 0 |
| 12 | 1 |
The calculator uses the Kaplan Meier survival estimator.
S(t) = ∏ (1 - di / ni)
Here, di is the number of events at time ti. ni is the number at risk just before that time. The product is taken across all event times up to the selected time point. Censored rows reduce the later risk set, but they do not directly reduce survival at their censoring time.
Survival function calculation from a dataset helps analysts estimate the probability that a subject remains event free beyond a given time. This page supports time to event analysis with censoring, ordered failures, and risk set tracking. It is useful in clinical research, reliability studies, customer churn analysis, and operational statistics where event timing matters.
A survival function, written as S(t), shows the chance that survival time exceeds t. When some observations are censored, a simple average is not enough. The Kaplan Meier approach handles incomplete follow up correctly. It updates survival only at observed event times and keeps censored cases in the risk set until their censoring time.
Start by loading a dataset with two columns. One column stores time values. The other stores event indicators. Use 1 for an event and 0 for censoring. You can paste comma separated values into the text area or upload a CSV file. The calculator can read headers, accept chosen column names, and return a detailed step table.
After submission, the result section appears above the form. It reports total records, total events, censored observations, final survival, and survival at the selected query time. The detailed table lists each unique time, subjects at risk, events, censored counts, conditional survival, and cumulative survival. This makes the estimation process transparent and easy to audit.
The method is especially helpful when follow up periods differ across records. It preserves information from partial observations instead of discarding them. That improves survival estimation quality and supports better statistical decisions. Analysts can compare cohorts, inspect early drop off, and review how censoring affects interpretation over time.
Because the output is tabular, reviewers can verify every Kaplan Meier step directly. That reduces black box confusion and makes the calculator suitable for classroom demonstrations, internal quality checks, exploratory studies, and reproducible reporting tasks today.
The example data table gives a quick starting point for testing. CSV export supports spreadsheet workflows, while PDF export helps documentation and sharing. Use this tool when you need an interpretable survival curve summary without building a separate script. It combines practical data handling, clear formulas, and reusable output for statistical reporting, teaching, and model validation.
It measures the probability that the event time is greater than a selected time value. In practice, it shows how likely a subject, machine, or customer remains event free beyond that point.
An event value of 1 means the event happened at that time. A censoring value of 0 means observation stopped without the event being observed, so the record still informs the risk set.
It uses the Kaplan Meier product limit estimator. The calculator groups equal times, counts events and censoring, updates the risk set, and multiplies conditional survival terms across event times.
Yes. You can upload a CSV file or paste comma separated values into the textarea. The calculator can read headers and lets you choose time and event columns.
Rows with the same time are grouped together. The calculator counts total events and censored records at that time, then applies one Kaplan Meier step using the full risk set before that time.
It is the cumulative survival estimate after processing all dataset times less than or equal to the query time. It helps you read survival probability at a specific time horizon.
CSV export is useful for spreadsheets, checking formulas, and further analysis. PDF export is useful for reports, teaching notes, review meetings, and sharing a stable snapshot of the results.
Use two clean columns: one numeric time column and one event indicator column. Keep times nonnegative, avoid blank rows, and encode event values clearly as 1 and 0 for consistent results.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.