Enter your survival data (time, event/censored status, optional groups), set significance level and CI, then calculate to get median survival time, log-rank p-value, and a breakdown of subjects, events, and censored cases.
Median Survival Time
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Log-Rank Test P-Value
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Total Subjects
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Total Events
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Censored Subjects
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Survival Probability at Median
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The Kaplan-Meier method is a non-parametric statistical technique used to estimate survival probabilities over time from time-to-event data. It accounts for censored observations where the exact event time is unknown.
Enter data with one row per subject in the format: Time,Event,Group. Use 1 for events and 0 for censored observations. Groups should be labeled consistently (e.g., A, B, Control, Treatment).
The log-rank test compares survival curves between groups to determine if there are statistically significant differences. A p-value less than your significance level suggests significant differences between groups.
Censoring occurs when the exact event time is unknown, typically because the study ended before the event occurred or the subject was lost to follow-up. Censored observations are marked as 0 in the Event column.
Median survival time is the time point at which the survival probability drops to 50%. If survival never reaches 50%, the median is considered not reached or undefined.
Yes, the Kaplan-Meier method can handle multiple groups. The log-rank test will compare all groups simultaneously, though pairwise comparisons may be needed for detailed analysis.
When the survival probability never reaches 50%, the median survival time cannot be calculated. This often occurs in studies with high survival rates or short follow-up periods.