Survival Analysis

Survival analysis reveals insights into time-to-event data, enhancing prediction and understanding of outcomes
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Survival Analyses Supported by Prism

Prism is already formatted for the survival analysis test you want to run:

Kaplan-Meier Survival Analysis

A simple and common method to estimate the survival curve.

Cox Proportional Hazards Regression

A model which allows additional variables to be included, assuming that the hazard ratio when comparing two groups is consistent over time.

Performing Simple Survival Analysis in Prism

See how to execute Kaplan-Meier 3 simple steps:
1

Enter Your Data

The data table is already correctly structured according to the analysis you are running. The table incorporates the time elapsed, survival status, and additional variables if you are running cox proportional hazards regression analysis. If you do not have data yet, there is a sample data set prepared for you.
2

Run Your Analysis

Select either Kaplain Meier or Cox Regression analysis based on your use case. Both analyses have preselected parameters.
3

Customize Your Visualizations

You can use Prism's Analysis Checklists to investigate the assumptions and reduce errors in your analysis. You can also choose from hundreds of customization options to adjust your graphs and charts for clarity on the survival times, associations, assumptions, and comparisons.

Now you can confidently present publication-worthy visuals that highlight your most important findings in an elegant, consumable format.

  • Graph Highlights & stars
  • Automatically Labelled Graphs
  • Improved Grouped Graphs

Involve your team at any step in the process.

Share your data, analyses, and graphs in one click.

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Survival Analysis Highlights

Prism is purpose-built for scientists to easily perform Survival Analysis, which estimates the lifespan of a population over the timespan of an experiment. More specifically, Survival curves graphically represent the data collected in these experiments, showing the relationship between elapsed time and survival probability. If survival curves for different groups or populations have been generated (using the Kaplan-Meier estimation method), these can be compared using the log rank test.

Survival Analysis can be used to answer many questions :

  • How much of a population will survive past a certain time?
  • At what rate will the event occur?
  • Should multiple variables be considered a cause?

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