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Related Experiment Videos

ORDMKV: a computer program fitting proportional odds model for multi-state Markov process

W Guo1, G Marshall

  • 1Department of Preventive Medicine and Biometrics, School of Medicine, University of Colorado Health Sciences Center, Denver 80262, USA.

Computer Methods and Programs in Biomedicine
|April 1, 1995
PubMed
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ORDMKV is a novel computer program for fitting multi-state disease models with ordinal structures. It simplifies complex disease progression analysis, offering a parsimonious approach for modeling patient health trajectories.

Area of Science:

  • Biostatistics
  • Computational Biology
  • Epidemiology

Background:

  • Disease progression is often characterized by stages of increasing severity.
  • Modeling these ordinal disease states requires specialized statistical approaches.
  • Existing models may be overly complex or lack efficiency for ordinal processes.

Purpose of the Study:

  • To introduce ORDMKV, a computer program for fitting multi-state discrete-time Markov models with ordinal structures.
  • To demonstrate the utility of ordinal response models in reducing parameter complexity for disease progression.
  • To enable the computation of survival curves in models with an absorbing terminal state.

Main Methods:

  • Development of a discrete-time Markov model incorporating an ordinal structure for k-stages.

Related Experiment Videos

  • Application of ordinal response models, specifically proportional odds models, to cumulative transition probabilities.
  • Estimation of regression coefficients using maximum likelihood via a quasi-Newton optimization algorithm.
  • Implementation in standard FORTRAN 77.

    • Main Results:

    • The ORDMKV program effectively fits multi-state ordinal disease models.
    • Ordinal response models significantly reduce parameter count compared to general or continuous-time models.
    • The model allows for the inclusion of an optional absorbing state for survival analysis.
    • Demonstrated application in identifying factors influencing diabetic retinopathy in young subjects.

    Conclusions:

    • ORDMKV provides an efficient and parsimonious method for analyzing ordinal disease progression.
    • The use of ordinal response models offers a significant advantage in statistical modeling of health states.
    • The program facilitates survival analysis and identification of disease progression factors.