On power and sample size calculations for likelihood ratio tests in generalized linear models
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Published on: June 25, 2019
1Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan 30050, Republic of China. gwshieh@cc.nctu.edu.tw
Abstract:
A direct extension of the approach described in Self, Mauritsen, and Ohara (1992, Biometrics 48, 31-39) for power and sample size calculations in generalized linear models is presented. The major feature of the proposed approach is that the modification accommodates both a finite and an infinite number of covariate configurations. Furthermore, for the approximation of the noncentrality of the noncentral chi-square distribution for the likelihood ratio statistic, a simplification is provided that not only reduces substantial computation but also maintains the accuracy. Simulation studies are conducted to assess the accuracy for various model configurations and covariate distributions.
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