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Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

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Published on: July 22, 2016

New agreement measures based on survival processes.

Ying Guo1, Ruosha Li, Limin Peng

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, U.S.A.

Biometrics
|July 13, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for assessing agreement in time-to-event data, crucial for comparing different measurement methods in biomedical research. The new chance-corrected concordance measure offers a robust way to evaluate survival outcome agreement.

Keywords:
Absolute differenceAgreementCensoringConcordance correlation coefficientCorrelated survival processesMultivariate survival times

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Area of Science:

  • Biomedical Sciences
  • Survival Analysis
  • Statistical Methods

Background:

  • Assessing agreement between measurement methods is vital in biomedical research.
  • Time-to-event data presents unique challenges for agreement assessment.
  • Existing methods may not adequately capture agreement for survival outcomes.

Purpose of the Study:

  • To propose a new framework for assessing agreement using survival processes.
  • To develop a chance-corrected concordance measure for correlated survival outcomes.
  • To extend the framework for multivariate and time-dependent agreement.

Main Methods:

  • Formulated a new agreement measure based on survival processes.
  • Developed a chance-corrected concordance for survival outcomes.
  • Proposed nonparametric estimation methods for the agreement measures.
  • Investigated multivariate and time-dependent extensions.

Main Results:

  • The proposed agreement measure offers a new perspective on correlated survival outcomes.
  • Nonparametric estimators are strongly consistent and asymptotically normal.
  • Simulation studies validate the performance of the estimators.
  • The methods are illustrated with a prostate cancer data example.

Conclusions:

  • The new framework provides a robust method for agreement assessment in time-to-event data.
  • The chance-corrected concordance measure is interpretable on an absolute distance scale.
  • The proposed methods are applicable to complex biomedical data, including time-dependent agreement.