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Failure inference from a marker process based on a bivariate Wiener model

G A Whitmore1, M J Crowder, J F Lawless

  • 1McGill University, Montreal, Canada.

Lifetime Data Analysis
|October 27, 1998
PubMed
Summary
This summary is machine-generated.

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This study introduces a new statistical model using a bivariate Wiener process to analyze failure times influenced by unobservable markers. The model simplifies calculations for failure time distributions and is adaptable for complex data scenarios.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Stochastic Processes

Background:

  • Existing models often link failure times to observable markers, which can be unrealistic.
  • Joint models for marker evolution and failure can involve complex calculations.
  • A need exists for more realistic and computationally tractable models for time-varying covariates and failure times.

Purpose of the Study:

  • To develop a novel statistical model for failure times influenced by unobservable, time-varying covariates.
  • To provide a model that simplifies the calculation of key characteristics like marginal failure time distributions.
  • To offer a flexible framework for analyzing complex data structures in survival analysis.

Main Methods:

  • Utilized a bivariate Wiener process, with one component for an observable marker and a second latent component for failure time.

Related Experiment Videos

  • Failure is modeled as the latent component crossing a threshold.
  • Developed methods for parametric and predictive inference, including model checking and an extension for composite markers.

    • Main Results:

    • The proposed bivariate Wiener process model yields simpler expressions for failure time characteristics.
    • The model is demonstrated to be easily fitted to common data types, including censored cases.
    • An extension allows for the integration of multiple candidate markers into a composite marker.

    Conclusions:

    • The bivariate Wiener process model offers a computationally efficient and realistic approach to analyzing failure times with latent covariates.
    • The model's flexibility and ease of fitting make it suitable for various applications in biostatistics and survival analysis.
    • The methodology is validated through simulation and a real-world case study.