Online Program

Cluster randomized trials and statistical power

Monday, November 4, 2013 : 12:30 p.m. - 12:50 p.m.

Stephen Lauer, Department of Biostatistics, School of Public Health, University of Massachusetts - Amherst, Amherst, MA
Nicholas G Reich, Ph.D, Division of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA
Ken P. Kleinman, ScD, Harvard Medical School/Harvard Pilgrim Health Care Institute
The cluster-randomized trial (CRT) is a common study design in public health research. In situations where dividing a group of individuals into treatment and controls is unethical or impossible, a CRT design maintains the strengths of a randomized study design. By comparing the outcomes of small populations (clusters), we can observe the impacts of interventions on the community as a whole. Public health researchers around the world have utilized CRTs to measure the effect of de-worming medication on school attendance, financial incentives on doctor absenteeism, and providing chlorine to waterholes.

The CRT can be a potent tool, however it is not without flaws. As with an individually randomized trial, it often requires a large sample size (i.e. many clusters) to achieve adequate levels of power for its results. Existing formulas to estimate power for a study design frequently rely on simplifications of the study design. Addressing common challenges that researchers face when calculating power – such as variability in cluster sizes and uncertainty in between-cluster variability – our goal is to illustrate how these features affect power and to show the utility of a simulation-based power calculation methodology.

Using R and the clusterPower package, we conducted a simulation study to quantify how between-cluster variance, treatment effects, number of clusters, and variability in cluster size can influence statistical power of a study. From this study, we derived concrete guidelines that can assist in the design phase of future CRTs, whether for testing a vaccine in Thailand or legislation in America.

Learning Areas:

Biostatistics, economics
Conduct evaluation related to programs, research, and other areas of practice

Learning Objectives:
Explain the design of cluster-randomized trials Discuss the usage of cluster randomized trials Assess the impact of selected variables on the cluster-randomized trial's power

Keyword(s): Methodology, Statistics

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a research assistant studying cluster-randomized trials. I am well-versed in the literature pertaining this subject and have recreated reports using this study design in statistical simulations. I am currently in the process of writing a paper illustrating my findings to be submitted for publication in the near future.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.