The Kaplan-Meier chart, also known as the Kaplan-Meier survival curve, is a graphical representation of survival analysis data. It is commonly used in medical research and other fields to analyse the probability of an event occurring over time.

The Kaplan-Meier chart is particularly useful when studying time-to-event data, where the event of interest could be anything from death, disease recurrence, or treatment failure to other significant outcomes. It provides a visual representation of the survival or event-free probability at different time points. It is a non-parametric method, which means that it does not make any assumptions about the underlying distribution of the data.

In business scenario Kaplan Meier charts can be used to track the survival rates of customers or employees;

in insurance use case these charts can be used to estimate the risk of death or other events, helping to set insurance premiums.

Here's how the Kaplan-Meier chart works:

**Data Collection**: Data is collected for a group of subjects who are followed over a specific time period. For example, in a clinical trial, patients may be monitored for the occurrence of a particular event or outcome.

**Time Intervals:** The time period is divided into discrete intervals, such as days, months, or years. These intervals are typically represented on the x-axis of the chart.

**Event Status:** For each subject, their event status is noted at each time interval. The event status can be coded as 1 if the event of interest occurred (e.g., death) or 0 if the event did not occur (e.g., still alive).

**Survival Probability Calculation**: The survival probability is calculated at each time interval. It represents the proportion of subjects who have not experienced the event up to that point in time. The initial survival probability is 1 (i.e., all subjects are event-free at the start).

**Cumulative Survival Probability**: The survival probabilities at each time interval are multiplied to obtain the cumulative survival probability. This cumulative probability is plotted on the y-axis of the chart.

**Omniscope configuration**

If your dataset contains the fields with time/ duration, strata number (your observation group) and event status you will be able to connect it to the **Survival Analysis block** from the Analytics section, in order to calculate survival probabilities and parameters needed for the Kaplan Meier visualisation.

Once the data is prepared we can proceed with the visualisation in the Report block.

Add the Kaplan Meier chart from the visuals library.

In the configuration options pick the fields for the Kaplan Meier basic settings.

**Split** - [time]

**Range** axis:

Range measure 1 - [lower]

Range measure 2 - [upper]

**Layer** - [strata]

**Line** - [surv]

There are more settings options - see the **example demo file attached. **In this project you can see and example of the data prep procedure, as well as the chart configuration.

The view also has a built-in functionality for **reference lines** (either fixed or dynamic formula values), on both x and y axis, making it easy to visually establish the median value for different strata. The median survival time is the time at which half of the subjects in a study have died/ were removed from the group.

This demo is also available from the Omniscope demo gallery, so you can explore the visuals without downloading the report.

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