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A statistical model for interpreting computerized dynamic posturography data.

Alan H Feiveson1, E Jeffrey Metter, William H Paloski

  • 1National Aeronautics and Space Administration, Johnson Space Center, Houston, TX 77058, USA. alan.h.feiveson1@jsc.nasa.gov

IEEE Transactions on Bio-Medical Engineering
|April 11, 2002
PubMed
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Computerized dynamic posturography (CDP) analysis faces challenges due to skewed equilibrium scores (ES) and zero-censoring from falls. A new stochastic model provides a robust statistical method for analyzing balance control data.

Area of Science:

  • Biomechanics
  • Statistics
  • Gerontology

Background:

  • Computerized dynamic posturography (CDP) is crucial for assessing balance control.
  • Equilibrium scores (ES) quantify CDP trial outcomes but often show skewed distributions.
  • Standard statistical models struggle with ES data due to skewness and zero-censoring from falls.

Purpose of the Study:

  • To develop and validate an alternative statistical methodology for analyzing equilibrium scores (ES) in computerized dynamic posturography (CDP).
  • To address the challenges posed by skewed distributions and mixed discrete-continuous nature of ES data.
  • To enable more accurate statistical inference on factors affecting balance control.

Main Methods:

  • A novel stochastic model was developed, extending the observed ES to a latent continuous random variable.
Keywords:
NASA Center JSCNASA Discipline Neuroscience

Related Experiment Videos

  • The model accounts for the conditional probability of balance loss based on the latent ES.
  • Quasi-maximum-likelihood estimation was used for model fitting and statistical inference.

    • Main Results:

    • The proposed stochastic model effectively handles the skewed and zero-censored distribution of ES.
    • This methodology allows for reliable assessment of explanatory variable effects on balance control.
    • The approach was demonstrated using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

    Conclusions:

    • The developed statistical methodology offers a more accurate approach to analyzing CDP data compared to standard methods.
    • This enhances the reliability of findings in studies investigating balance control.
    • The model provides a robust framework for understanding factors influencing postural stability.