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Related Concept Videos

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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Time-to-Event Endpoints in Imaging Biomarker Studies.

Ruizhe Chen1, Hao Wang1

  • 1The Sidney Kimmel Comprehensive Cancer Center, Division of Quantitative Sciences, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Journal of Magnetic Resonance Imaging : JMRI
|May 13, 2024
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Summary

This review explains time-to-event endpoints for imaging biomarker research. It highlights potential pitfalls and discusses the benefits of using overall survival or progression-free survival in clinical studies.

Keywords:
Imaging biomarkersOncologyOverall survivalProgression‐free survivalSurvival data analysisTime‐to‐event endpoints

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Area of Science:

  • Radiology
  • Medical Imaging
  • Biostatistics

Background:

  • Time-to-event endpoints are crucial for assessing patient outcomes and prognosis in clinical research.
  • Imaging biomarkers are increasingly evaluated for their prognostic and predictive value in various study settings.
  • Understanding these endpoints is essential for accurate interpretation of imaging biomarker studies.

Purpose of the Study:

  • To provide an educational review of time-to-event endpoints for radiologists.
  • To identify and discuss potential pitfalls in applying these endpoints to imaging biomarker research.
  • To review the benefits and selection considerations for overall survival and progression-free survival as primary endpoints.

Main Methods:

  • Educational review of existing literature and concepts.
  • Discussion of fundamental principles of time-to-event analysis.
  • Comparative analysis of overall survival versus progression-free survival.

Main Results:

  • Key concepts of time-to-event endpoints are clarified.
  • Common challenges and pitfalls in imaging biomarker research are identified.
  • Guidance on selecting appropriate endpoints (overall survival vs. progression-free survival) is provided.

Conclusions:

  • Accurate understanding of time-to-event endpoints is vital for robust imaging biomarker research.
  • Awareness of potential pitfalls can improve study design and interpretation.
  • Informed endpoint selection enhances the clinical utility of imaging biomarkers.