<p style="margin-bottom: 3pt; margin-top: 3pt; line-height: 1;">Power calculations for complex designed clinical trials using linear mixed models

Abstract

Power calculation is a crucial part of planning a clinical trial to ensure that it is capable of detecting a clinically and statistically significant treatment difference. Complex designed veterinary clinical trials considered in this report have structures that could be naturally handled by linear mixed models by accounting for different sources of variation through the inclusion of random effects. However, definitive formulations for power calculations using linear mixed models do not exist for most cases. Thus, the primary aim of the investigator is to develop SAS macros that would generate data according to common experimental settings, and make power calculation possible for employed linear mixed models through extensive simulations. Superiority testing was done through approximate F-test for fixed effects in Proc Mixed and Proc Glimmix for continuous and binary data, respectively. For non-inferiority testing of continuous data, approximate t-test confidence interval was constructed around the treatment difference and was compared to the clinically acceptable margin. However, for non-inferiority testing of binary data, the clinically acceptable margin of difference is usually expressed in difference of proportions or odds ratio, while the confidence interval for treatment difference constructed by SAS is in the logit scale. Three methods then were proposed in order to conduct non-inferiority testing in this case, which were constructing the CI for difference of proportions (Independence), CI for difference