Type I error control for cluster randomized trials under varying small sample structures

Simple sample size calculation for cluster-randomized trials.

BACKGROUND Cluster-randomized trials, in which health interventions are allocated randomly to intact clusters or communities rather than to individual subjects, are increasingly being used to evaluate disease control strategies both in industrialized and in developing countries. Sample size computations for such trials need to take into account between-cluster variation, but field epidemiologis...

Methods for sample size determination in cluster randomized trials

BACKGROUND The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternativ...

Type I Error Control for Tree Classification

Binary tree classification has been useful for classifying the whole population based on the levels of outcome variable that is associated with chosen predictors. Often we start a classification with a large number of candidate predictors, and each predictor takes a number of different cutoff values. Because of these types of multiplicity, binary tree classification method is subject to severe ...

Cluster Unit Randomized Trials

Cluster randomized controlled trials.

Cluster randomized controlled trial (RCT), in which groups or clusters of individuals rather than individuals themselves are randomized, are increasingly common. Indeed, for the evaluation of certain types of intervention (such as those used in health promotion and educational interventions) a cluster randomized trial is virtually the only valid approach. However, cluster trials are generally m...