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Change-point diagnostics in competing risks models: Two posterior predictive p-value approaches.

Chen-Pin Wang1, Malay Ghosh2

  • 1Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, USA.

Test (Madrid, Spain)
|November 14, 2014
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Summary

This study introduces a Bayesian method to detect incorrect change-point assumptions in competing risks models. The diagnostic procedure effectively identifies missed change-points using posterior predictive p-values.

Keywords:
Change-pointCompeting risksPosterior predictive p-values

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Competing risks models are crucial for analyzing time-to-event data with multiple outcomes.
  • Change-point assumptions are vital for accurately modeling distributional heterogeneity.
  • Misspecification of change-points can lead to inaccurate survival distribution or cause-specific probability estimates.

Purpose of the Study:

  • To develop a Bayesian diagnostic procedure for assessing change-point assumptions within competing risks models.
  • To quantify model departure arising from misspecified change-points.
  • To formalize a diagnostic procedure based on posterior predictive p-values.

Main Methods:

  • Utilizes a cause-specific model framework with added change-points for distributional heterogeneity.
  • Employs cumulative-sum statistics to quantify model departure between adjacent change-points.
  • Assesses asymptotic behavior using posterior predictive p-values and partial posterior predictive p-values.

Main Results:

  • Both posterior predictive p-values and partial posterior predictive p-values show maximum deviation from 0.5 at the missed change-point.
  • The proposed diagnostic procedure effectively identifies erroneously assumed change-points.
  • The statistical power of these p-value types for change-point detection is analyzed.

Conclusions:

  • The Bayesian diagnostic procedure offers a robust method for validating change-point assumptions in competing risks models.
  • Posterior predictive p-values serve as effective indicators for detecting misspecified change-points.
  • This approach enhances the reliability of survival analysis in the presence of competing risks.