Type of Document 
Dissertation 
Author 
Li, Yinmei

Author's Email Address 
yli1@lsu.edu 
URN 
etd03312006100431 
Title 
Power Analysis for a Mixed Effects Logistic Regression Model 
Degree 
Doctor of Philosophy (Ph.D.) 
Department 
Pathobiological Sciences (Veterinary Medical Sciences) 
Advisory Committee 
Advisor Name 
Title 
Daniel Scholl 
Committee CoChair 
James Miller 
Committee CoChair 
Giselle Hosgood 
Committee Member 
Luis Escobar 
Committee Member 
Martin HughJones 
Committee Member 
Guoli Ding 
Dean's Representative 

Keywords 
 bovine immunodeficiency virus
 logisticnormal model
 clustered binary data
 power analysis

Date of Defense 
20060321 
Availability 
unrestricted 
Abstract
In herd health studies, the mixed effects logistic regression model with random herd effects are commonly used for modeling clustered binary data. These models are well developed and widely used in the literature, among which is the logisticnormal regression model. In contrast to the rich literature in modeling methods, the sample size/power analysis methods for such mixed effects models are sparse. The sample size/power analysis method for the logisticnormal regression model is not readily available. This study is to develop a power analysis/sample size estimation method for the logisticnormal regression model. Extended from the sample size method for the likelihood ratio test in the generalized linear models (Self et al., 1992), a power analysis method for the logisticnormal model is developed based on a noncentral chisquare approximation to the distribution of the likelihood ratio statistic. The method described in this dissertation can be applied to both exchangeable and nonexchangeable responses. The power curves are presented with respect to the change of each of the planning values while holding other planning values fixed for two examples of the logisticnormal model containing one random cluster effect. The results from this proposed sample size/power analysis method for the logisticnormal model were compared to the results from the method for the fixed effects logistic regression model. For a given total sample size and the same applicable planning values, the power for the logisticnormal regression model is smaller than that for the fixed effects logistic regression model, suggesting that the minimum required sample size calculated from using the method for the fixed effects model is too small to achieve the desired power when the logisticnormal model is to be used in data analysis.

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