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Statistical methods for dependent competing risks

M L Moeschberger1, J P Klein

  • 1Department of Preventive Medicine, Ohio State University, Columbus 43210, USA.

Lifetime Data Analysis
|January 1, 1995
PubMed
Summary
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This study surveys methods for analyzing time-to-event data when competing risks are present. It addresses challenges in estimating survival functions when these risks are dependent, a common issue in medical and biological research.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Research

Background:

  • Time-to-event data is crucial in biological and medical studies, analyzing events like disease onset, remission, or death.
  • Competing risks, such as withdrawal or death from other causes, often prevent the direct measurement of the primary event of interest.
  • Standard methods like Kaplan-Meier and Nelson-Aalen estimators assume independence between censoring and event times.

Purpose of the Study:

  • To survey existing statistical literature on dependent competing risks in survival analysis.
  • To provide an overview of methods for estimating survival functions when competing risks are not independent of the event of interest.

Main Methods:

  • Literature review and synthesis of statistical methodologies.

Related Experiment Videos

  • Focus on approaches addressing the dependency between competing risks and the primary event.
  • Discussion of challenges in survival function estimation under dependent competing risks.

    • Main Results:

    • Identifies a gap in generally acceptable methods for survival function estimation when competing risks are dependent.
    • Highlights the scattered nature of research on dependent competing risks in statistical literature.
    • Presents a survey of various approaches developed to tackle this complex problem.

    Conclusions:

    • Dependent competing risks pose significant challenges to standard survival analysis techniques.
    • Further research and standardized methods are needed for accurate survival function estimation in the presence of dependent competing risks.
    • This survey serves as a reference for understanding the current state of research in this specialized area.