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Parametric likelihoods for multiple non-fatal competing risks and death

Y Shen1, P F Thall

  • 1Department of Biomathematics, UT M.D. Anderson Cancer Center, Houston, Texas 77030, USA.

Statistics in Medicine
|June 5, 1998
PubMed
Summary
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This study introduces a novel statistical model for analyzing competing risks in clinical trials, accounting for complex associations between non-fatal events and survival. The model improves survival estimates in fatal disease research.

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Survival Analysis

Background:

  • Clinical trials for fatal diseases often assess non-fatal events alongside survival to understand morbidity and improve survival estimates.
  • Analyzing multivariate time-to-event data is complicated by potential associations between non-fatal events and residual survival, differing distributions for death, and censoring.
  • Existing statistical methods often violate assumptions due to the mixture of survival distributions arising from antecedent non-fatal events.

Purpose of the Study:

  • To develop a general parametric model for analyzing multiple non-fatal competing risks and death in clinical trials.
  • To account for positive or negative associations between non-fatal event times and subsequent survival.
  • To accommodate covariates and administrative censoring within the competing risks framework.

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Main Methods:

  • A general parametric model for multiple non-fatal competing risks and death was developed.
  • The model incorporates positive or negative associations between non-fatal event times and residual survival.
  • Event time distributions were specified using a three-parameter generalized odds rate model, combined via a bivariate generalized von Mises distribution.

Main Results:

  • The proposed model effectively handles the complexities of competing risks and survival data in clinical trials.
  • Demonstrated application to clinical trial data from colon cancer and acute leukemia studies.
  • The model provides a robust framework for analyzing time-to-event data with competing risks.

Conclusions:

  • The developed parametric model offers a flexible approach to analyzing multivariate time-to-event data with competing risks.
  • This methodology is crucial for accurate survival estimation and morbidity characterization in clinical trials of fatal diseases.
  • The approach is applicable to various diseases, including cancer and leukemia, enhancing clinical trial analysis.