Abstract
Researchers in the agricultural and biological sciences often conduct experiments with repeated measures and categorical response variables. Recent advances in statisticalcomputing have made several options available to analyze data from these experiments. For example, SAS has several procedures based on generalized mixed model theory. These include PROC GENMOD, MIXED, NLMIXED, and the GLIMMIX macro. Inference for these procedures depends on asymptotic theory. While statistics literature contains some information about the small-sample behavior, there is much that remains unknown. This presentation will focus on Bernoulli response variables. Power characteristics are compared via simulation for several scenarios involving relatively small repeated measures experiments.
Keywords
GLMM, binary data, repeated measures, GEE, pseudo-likelihood, SAS procedures, power
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Beckman, Matthew and Stroup, Walter W.
(2003).
"SMALL SAMPLE POWER CHARACTERISTICS OF GENERALIZED MIXED MODEL PROCEDURES FOR BINARY REPEATED MEASURES DATA USING SAS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1172
SMALL SAMPLE POWER CHARACTERISTICS OF GENERALIZED MIXED MODEL PROCEDURES FOR BINARY REPEATED MEASURES DATA USING SAS
Researchers in the agricultural and biological sciences often conduct experiments with repeated measures and categorical response variables. Recent advances in statisticalcomputing have made several options available to analyze data from these experiments. For example, SAS has several procedures based on generalized mixed model theory. These include PROC GENMOD, MIXED, NLMIXED, and the GLIMMIX macro. Inference for these procedures depends on asymptotic theory. While statistics literature contains some information about the small-sample behavior, there is much that remains unknown. This presentation will focus on Bernoulli response variables. Power characteristics are compared via simulation for several scenarios involving relatively small repeated measures experiments.
