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A diagnostic for Cox regression with discrete failure-time models.

C B Parker1, E R Delong

  • 1Research Triangle Institute, P.O. Box 12194, Research Triangle Park, North Carolina 27709, USA. rette@rti.org

Biometrics
|February 24, 2001
PubMed
Summary

This study introduces an improved analytical method for estimating statistical model parameters by extending the augmentation approach. This enhances the accuracy of discrete failure-time models, even with time-dependent covariates.

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Maximum likelihood parameter estimates are crucial for regression diagnostics and robust covariance estimation.
  • Existing analytical approximations for delete-one statistics work for many likelihoods but not general conditional or Cox partial likelihoods.
  • A previous augmentation approach offered an alternative for these complex likelihoods.

Purpose of the Study:

  • To extend the analytic augmentation approach for computing delete-one statistics.
  • To explicitly address discrete failure-time models where individuals can appear in multiple risk sets.
  • To incorporate time-dependent covariates within this framework.

Main Methods:

  • Extending an analytic approximation method based on an augmented design matrix.

Related Experiment Videos

  • Applying the method to discrete failure-time models with multiple event times per subject.
  • Handling time-dependent covariates within the augmented model.

    • Main Results:

    • The extended augmentation approach provides accurate analytical approximations for delete-one statistics in discrete failure-time models.
    • This method effectively handles individuals contributing information over multiple time points.
    • The approach accommodates time-dependent covariates without additional computational cost.

    Conclusions:

    • The proposed augmentation method offers an efficient and accurate way to compute delete-one statistics for complex survival models.
    • This technique improves statistical diagnostics and robust estimation in discrete failure-time analyses.
    • The extension is computationally advantageous, requiring no extra resources while enhancing results.