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Updated: Jun 9, 2026

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

Comparing survival curves using an easy to interpret statistic.

Kenneth R Hess¹

¹Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77230-1402, USA.

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research

|August 25, 2010

Summary

This summary is machine-generated.

This study presents a biostatistics measure for comparing survival curves. A value of 0.95 means a patient on treatment A has a 95% chance of outliving a patient on treatment B.

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Area of Science:

Biostatistics
Survival Analysis
Clinical Trials

Background:

Comparing survival data is crucial in clinical research.
Existing methods may lack clear interpretation.
A need exists for intuitive survival curve comparison statistics.

Purpose of the Study:

To introduce and explain a historically significant statistic for comparing two survival curves.
To highlight its clear and interpretable meaning in clinical contexts.
To demonstrate its utility and accessibility for researchers.

Main Methods:

The statistic quantifies the probability that a random patient from group A survives longer than a random patient from group B.
It is applicable to right-censored survival data, a common feature in medical studies.
The method has historical roots dating back to the 1950s and was extended in the 1960s.

Main Results:

The statistic provides a direct, probabilistic interpretation of differences between survival distributions.
A value of 0.95 indicates a 95% chance that a patient on treatment A survives longer than a patient on treatment B.
The measure is shown to be a convenient and useful tool for assessing treatment effects.

Conclusions:

This statistic offers a clear and intuitive method for comparing survival outcomes.
Its long history and adaptability to censored data make it a robust biostatistical tool.
Readily available software ensures its practical application in analyzing survival data.