A Multinomial Model for Identifying Significant Pure-Tone Threshold Shifts Purpose Significant threshold differences on retest for pure-tone audiometry are often evaluated by application of ad hoc rules, such as a shift in a pure-tone average or in 2 adjacent frequencies that exceeds a predefined amount. Rules that are so derived do not consider the probability of observing a particular ... Research Article
Research Article  |   December 01, 2007
A Multinomial Model for Identifying Significant Pure-Tone Threshold Shifts
 
Author Affiliations & Notes
  • Robert S. Schlauch
    University of Minnesota, Minneapolis
  • Edward Carney
    University of Minnesota, Minneapolis
  • Contact author: Robert S. Schlauch, Department of Speech-Language-Hearing Sciences, University of Minnesota, 115 Shevlin Hall, 164 Pillsbury Drive, SE, Minneapolis, MN 55455. E-mail: schla001@umn.edu.
Article Information
Hearing Disorders / Hearing / Research Articles
Research Article   |   December 01, 2007
A Multinomial Model for Identifying Significant Pure-Tone Threshold Shifts
Journal of Speech, Language, and Hearing Research, December 2007, Vol. 50, 1391-1403. doi:10.1044/1092-4388(2007/097)
History: Received July 18, 2006 , Accepted April 27, 2007
 
Journal of Speech, Language, and Hearing Research, December 2007, Vol. 50, 1391-1403. doi:10.1044/1092-4388(2007/097)
History: Received July 18, 2006; Accepted April 27, 2007
Web of Science® Times Cited: 9

Purpose Significant threshold differences on retest for pure-tone audiometry are often evaluated by application of ad hoc rules, such as a shift in a pure-tone average or in 2 adjacent frequencies that exceeds a predefined amount. Rules that are so derived do not consider the probability of observing a particular audiogram.

Methods A general solution for evaluating threshold differences on retest was developed on the basis of multinomial probabilities. The model uses the standard deviation of inter-test differences for 1 frequency as a parameter of the underlying Gaussian distribution of test results. The number of test frequencies, the categories of threshold change, and the probability of each category’s occurrence are used to calculate the probability that a given pattern of threshold differences on retest (or 1 rarer) could occur by chance.

Results The multinomial model was compared with 2 other methods for identifying threshold shifts in persons exposed to high sound pressure levels during concerts. The multinomial model identified the same audiograms as the ad hoc methods.

Conclusion Tables developed using a multinomial model can provide a clinical tool for evaluating audiograms by identifying statistically significant patterns of test–retest differences in hearing thresholds.

Acknowledgments
This work was supported, in part, by a grant from the Graduate School of the University of Minnesota to the first author. We thank David Opperman for consultation about the data in Opperman et al. (2006)  and Daniel Zelterman for discussions during an early stage of this work.
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