Simulation study of power and sample size for repeated measures with multinomial outcomes: an application to sound direction identification experiments (SDIE)

• Published in: Statistics in medicine Vol. 30; no. 19; pp. 2451 - 2466
• Main Authors: Jiang, Dingfeng, Oleson, Jacob J.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 30.08.2011; Wiley Subscription Services, Inc
• Subjects: Comparative analysis; Computer Simulation; Ears & hearing; Generalized linear models; GLMM; Hearing aids; Humans; Linear Models; Models, Statistical; multinomial distribution; Sample Size; Simulation; Sound Localization; spatial correlation
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.4302

This study focuses on sample size determination in repeated measures studies with multinomial outcomes from multiple factors. In settings where multiple factors have repeated measures, a single subject could have hundreds of observations. Sample size selection may then refer to the number of subjects, the number of levels within a factor, or the number of repetitions within the level. We simulate multinomial data through a generalized linear mixed model (GLMM) with and without overdispersion, compute the empirical power of detecting group difference for several analytical methods and contrast their performance in group comparison studies with repeated multinomial data. We use four spatial functions to model the spatial correlation structures among observations. We evaluate the factors affecting the power under various scenarios. We also present a dataset typical in hearing studies for sound localization, in which a spatially distributed array of audio loudspeakers plays multiple sounds in order to compare two programming schemes for a hearing aid device. Copyright © 2011 John Wiley & Sons, Ltd.