There are no patents, products in development or marketed products to declare. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: This study was partly funded by Sage Products, Inc. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention. NGR and TMP were funded by the ResPECT study ( ID: NCT01249625) through an interagency agreement between the Centers for Disease Control and the United States Department of Veterans Affairs (CDC IAA# 09FED905876). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: DO, AAM, TMP were funded in part by grants from Sage Products, Inc. Received: OctoAccepted: MaPublished: April 27, 2012Ĭopyright: © 2012 Reich et al. Vermund, Vanderbilt University, United States of America More research is needed to develop standardized and recommended methodology for cluster-randomized crossover studies.Ĭitation: Reich NG, Myers JA, Obeng D, Milstone AM, Perl TM (2012) Empirical Power and Sample Size Calculations for Cluster-Randomized and Cluster-Randomized Crossover Studies. This work is the first to establish a universal method for calculating power for both cluster-randomized and cluster-randomized clinical trials. The clusterPower package could play an important role in the design of future cluster-randomized and cluster-randomized crossover studies. ![]() We give four examples of using the software in practice. ![]() Our simulation framework is easy to implement and users may customize the methods used for data analysis. We have implemented this framework in the clusterPower software package for R, freely available online from the Comprehensive R Archive Network. We present a general framework for estimating power via simulation in cluster-randomized studies with or without one or more crossover periods. ![]() We address one particular aspect of cluster-randomized and cluster-randomized crossover trial design: estimating statistical power. While the cluster-randomized crossover trial has become a popular tool, standards of design, analysis, reporting and implementation have not been established for this emergent design. In addition, the cluster-randomized crossover design has been touted as a methodological advance that can increase efficiency of cluster-randomized studies in certain situations. In recent years, the number of studies using a cluster-randomized design has grown dramatically.
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