Description:

Sample Size Determination for Multilevel Hierarchical Designs Using Generalized Linear Mixed Models

In this talk, we will discuss appropriate statistical methods for determining the required sample size while comparing the efficacy of an intervention to a control with repeated response outcomes. Our proposed methodology incorporates the complexity of the hierarchical nature of underlying designs and provides solutions when varying attrition rates are present over time. We will explore how the between-subject variability and attrition rates jointly influence the computation of sample size formula. Our approach addresses how sample sizes change with respect to sampling proportions, number of groups, and attrition rates over time. In addition, we will discuss a cost model, as an aid in selecting the most parsimonious of several possible competing models (i.e., different combinations of centers, subjects within centers, and measurement occasions). We derive sample size requirements (i.e., power characteristics) for a test of group-by-time interaction(s) for designs based on either subject-level or cluster-level randomization. The general methodology will be illustrated using two characteristic examples.