Survival Analysis (Kaplan-Meier)
Generate survival curves, calculate Median Survival Time, and perform Log-Rank tests for multiple groups.
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schedule SAS Time Unit Converter
1 Month = 30.4375 days (FDA & SAS Standard)
timeline Kaplan-Meier Analysis
Compare up to 4 groups. Generate Median Survival Time, Log-Rank P-value & Plot.
menu_book Comprehensive Guide to Kaplan-Meier
In clinical trials, oncology research, and longitudinal animal studies, researchers frequently measure "time-to-event" data. This could be the time until death (Overall Survival), the time until a tumor doubles in volume (Progression-Free Survival), or the time until a disease relapses. The Kaplan-Meier (KM) Estimator is the non-parametric statistical gold standard for analyzing this type of incomplete data.
📝 Crucial Step: Data Coding (0 vs 1)
The power of survival analysis lies in its ability to handle incomplete observation periods. Accurate status coding is strictly required in the input boxes above.
radio_button_checked Event (Code 1)
The specific event of interest actually occurred (e.g., the subject died, or the tumor reached the experimental endpoint). The survival probability curve will visually drop at this exact time point.
radio_button_unchecked Censored (Code 0)
The subject did NOT experience the event during the observation. This happens if the subject dropped out, was lost to follow-up, or the study ended while they were alive. The curve does not drop here, but the denominator (number of subjects "at risk") decreases for future calculations.
📉 How to Interpret the Kaplan-Meier Plot
The generated visual plot illustrates the probability of surviving (or avoiding the specific event) as time progresses.
- The Y-Axis: Represents the cumulative survival probability, ranging from 0.0 to 1.0 (or 0% to 100%). At Time = 0, the probability is always 1.0.
- The Vertical Steps: A drop in the line indicates that one or more events (Code 1) occurred. The magnitude of the drop is dynamically calculated based on how many subjects are currently remaining in the risk pool.
- The Horizontal Lines & Ticks: Flat periods represent time passing without events. Small vertical ticks on the line indicate points where a subject was censored (Code 0).
timer Clinical Significance of "Median Survival Time"
Instead of using a simple mathematical mean, survival studies rely on the Median Survival Time. This represents the precise time point at which the survival probability curve crosses the 0.5 (50%) threshold—meaning exactly half of the cohort has experienced the event.
analytics The Log-Rank Test (Statistical Validation)
While the KM plot provides an intuitive visual representation of the data, you cannot claim a drug is effective just by looking at a graph. Our calculator automatically performs the Log-Rank Test (Mantel-Cox test). This hypothesis test generates a Chi-Square statistic and a precise P-value, determining if the survival curves of different groups (e.g., Control vs. Treatment) are statistically significantly different from one another.
Frequently Asked Questions (FAQ)
Why can't I just calculate the average survival time and use a T-Test?
A standard T-test or ANOVA cannot handle "censored" data. If a mouse is still alive at the end of a 100-day study, its true survival time is unknown (it is >100 days). Averaging it as exactly 100 days artificially lowers the true mean and severely biases your results. The Kaplan-Meier method mathematically accounts for this missing information.
Why use the SAS conversion rate (30.4375) for months?
In rigorous clinical and preclinical reporting (including FDA submissions), dividing days by exactly 30 or 31 is inaccurate over multi-year studies due to leap years. The SAS standard normalizes a month as exactly 365.25 days / 12 months = 30.4375 days. Our built-in converter ensures your timelines meet this publication standard.
What does "Right-Censoring" mean?
Right-censoring is the most common type of censoring in biological survival analysis. It simply means that the true time of the event occurs somewhere to the "right" of your data timeline (in the future, after your observation period has already ended).