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Related Experiment Videos

Modeling event times with multiple outcomes using the Wiener process with drift.

Julie Horrocks1, Mary E Thompson

  • 1Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada. jhorrock@uoguelph.ca

Lifetime Data Analysis
|May 8, 2004
PubMed
Summary

This study introduces a new parametric regression model for hospital length of stay (LOS) that accounts for multiple outcomes and unexplained variability. The model uses a Wiener process and competing risks analysis to better understand patient disposition and resource utilization.

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Survival Analysis

Background:

  • Length of stay (LOS) is a key metric in health services research for resource consumption and efficiency.
  • LOS data often exhibits skewed distributions and multiple potential outcomes (discharge, death, transfer).
  • Existing models struggle with unexplained heterogeneity even after covariate inclusion.

Purpose of the Study:

  • To develop a novel parametric regression model for hospital length of stay (LOS).
  • To address the complexities of multiple outcomes and unexplained heterogeneity in LOS data.
  • To formalize competing risks analysis within a health services research context.

Main Methods:

  • Developed a parametric regression model based on a Wiener process with drift.

Related Experiment Videos

  • The model considers time to hitting one of two barriers (healthy discharge or death).
  • Employed maximum likelihood estimation using an approximation of the density function.

    • Main Results:

    • The model provides expressions for expected LOS, conditional expectations given outcome, and outcome probabilities.
    • Demonstrated the model's utility using administrative healthcare data.
    • Discussed extensions for third outcomes and mixture models for heterogeneity.

    Conclusions:

    • The proposed Wiener process-based competing risks model offers a robust framework for analyzing hospital length of stay.
    • This approach enhances understanding of factors influencing patient disposition and resource use.
    • The model effectively handles multiple outcomes and remaining unexplained variability in LOS data.