Planning a cluster randomized trial with unequal cluster sizes: practical issues involving continuous outcomes

BACKGROUND: Cluster randomization design is increasingly used for the evaluation of health-care, screeening or educational interventions. At the planning stage, sample size calculations usually consider an average cluster size without taking into account any potential imbalance in cluster size. However, there may exist high discrepancies in cluster sizes. METHODS: We performed simulations to study the impact of an imbalance in cluster size on power. We determined by simulations to which extent four methods proposed to adapt the sample size calculations to a pre-specified imbalance in cluster size could lead to adequately powered trials. RESULTS: We showed that an imbalance in cluster size can be of high influence on the power in the case of severe imbalance, particularly if the number of clusters is low and/or the intraclass correlation coefficient is high. In the case of a severe imbalance, our simulations confirmed that the minimum variance weights correction of the variation inflaction factor (VIF) used in the sample size calculations has the best properties. CONCLUSION: Publication of cluster sizes is important to assess the real power of the trial which was conducted and to help designing future trials. We derived an adaptation of the VIF from the minimum variance weights correction to be used in case the imbalance can be a priori formulated such as "a proportion (γ) of clusters actually recruit a proportion (τ) of subjects to be included (γ ≤ τ)"

A priori postulated and real power in cluster randomized trials: mind the gap

BACKGROUND: Cluster randomization design is increasingly used for the evaluation of health-care, screening or educational interventions. The intraclass correlation coefficient (ICC) defines the clustering effect and be specified during planning. The aim of this work is to study the influence of the ICC on power in cluster randomized trials. METHODS: Power contour graphs were drawn to illustrate the loss in power induced by an underestimation of the ICC when planning trials. We also derived the maximum achievable power given a specified ICC. RESULTS: The magnitude of the ICC can have a major impact on power, and with low numbers of clusters, 80% power may not be achievable. CONCLUSION: Underestimating the ICC during planning cluster randomized trials can lead to a seriously underpowered trial. Publication of a priori postulated and a posteriori estimated ICCs is necessary for a more objective reading: negative trial results may be the consequence of a loss of power due to a mis-specification of the ICC

High blood pressure in school children: prevalence and risk factors

BACKGROUND: The purpose of this study was to determine the prevalence of high blood pressure (HBP) and associated risk factors in school children 8 to 13 years of age. METHODS: Elementary school children (n = 1,066) were examined. Associations between HBP, body mass index (BMI), gender, ethnicity, and acanthosis nigricans (AN) were investigated using a school based cross-sectional study. Blood pressure was measured and the 95(th )percentile was used to determine HBP. Comparisons between children with and without HBP were utilized. The crude and multiple logistic regression adjusted odds ratios were used as measures of association. RESULTS: Females, Hispanics, overweight children, and children with AN had an increased likelihood of HBP. Overweight children (BMI ≥ 85(th )percentile) and those with AN were at least twice as likely to present with HBP after controlling for confounding factors. CONCLUSION: Twenty one percent of school children had HBP, especially the prevalence was higher among the overweight and Hispanic group. The association identified here can be used as independent markers for increased likelihood of HBP in children