Kaplan-Meier curves

Distributions of open-ended time intervals

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Updated at October 17th, 2019

Protobi can create Kaplan-Meier survival curves based on date/time data. These are useful to describe distributions of time intervals, particularly when the intervals may be ongoing.

For example, below shows the distribution of time on first line regimen from a patient chart review. The challenge is that many patients who started first line therapy are still on their first line therapy, and the exact duration is unknown and increasing.

In terms closer to home, this is like estimating the life of light bulbs when many bulbs are still bright and presumably may be so for some time yet.

The Kaplan-Meier Survival estimator is a non-parametric statistic that handles open time intervals. The term "non-parametric" is statistical jargon that means "just the facts, no modeling."

The key information required is two fields, start date and end date, plus a boolean field indicating if the end date is closed  or "open" (i.e., still continuing at the time).

The figure above shows the median time on 1L therapy is 26 months, considering patients who have and have not actually finished.

To create a Kaplan-Meier curve in Protobi, set the chartType to "KaplanMeier" and include an attribute "intervals" which defines an array of relevant time intervals.

Each interval is an object specifying its key ,title, fields that define the start date and end date, and a boolean field indicating whether the interval is censored.

    "chartType": "KaplanMeier",
    "intervals": [
        {
            "censoredRight": "censored_reg1",
            "start": "dt_start_1L",
            "end": "dt_end_1L",
            "title": "1L",
            "key": "1L"
        }
    ],

These charts can also be cross-tabbed to compare differences by another variable like setting or location or market segment. Drag a variable title to the header and drop to create a crosstab:

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