Power analysis for clustered non-continuous responses in multicenter trials

Abstract

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.

Keywords:

• GEE
• GLIMMIX
• intraclass correlation
• marginal models
• NLMIXED

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

The work was supported in part by R34MH096854 (Knox, Tang and Tu), DA027521 (Tang and Tu) and GM108337 (Tang and Tu) from the National Institutes of Health as well as by funds from the Veteran Administration through the Center of Excellence for Suicide Prevention in Canandaigua VA Medical Center (Tu and Arora).

Supplemental data and research materials

Supplemental data for this article can be accessed at doi:10.1080/02664763.2015.1089218.