Intraclass correlation coefficients for cluster randomized trials in primary care: The cholesterol education and research trial (CEART)
Intraclass correlation coefficients for cluster randomized trials in primary care: The cholesterol education and research trial (CEART)
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DOI：
10.1016/j.cct.2005.01.002
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发表时间：
2005-04-01
影响因子：
2.2
通讯作者：
Eaton, CB
中科院分区：
文献类型：
--
作者：
Parker, DR;Evangelou, E;Eaton, CB
整群随机试验越来越多地被用于初级保健研究。这些试验的主要特点是，患者被嵌套在大型集群中，例如医生诊所或社区，干预应用于集群。本研究设计需要计算组内相关系数，以确定所需的样本量。这项研究的目的是确定初级保健实践水平上一些结果指标的组内相关系数。CEART研究是一项随机试验，以初级保健医生实践为随机单位，以患者为数据收集单位，测试将ATP III指南转化为临床实践的有效性。体重、总胆固醇、低密度脂蛋白、非高密度脂蛋白、葡萄糖、肌酐和非高密度脂蛋白目标百分比的组内相关系数(ICC)为<；0.02，设计效应范围为1.0至2.3。对于吸烟状况、体质量指数、血压、高密度脂蛋白胆固醇、甘油三酯、总胆固醇/高密度脂蛋白比值和低密度脂蛋白目标值的百分比，ICC为0.02~0.047，设计效应为2.64.1。身高和舒张压的ICC最大(0.05-0.12)，设计效应最大(4.4-9.4)。这些发现表明，与本研究中评估的大多数结果的简单随机化相比，分组随机化可以显著增加为选定结果(如舒张压研究)保持足够的统计能力所需的样本量，其中设计效果为小到中等。总体而言，提供的ICC在计算初级保健水平的样本量时将是有用的。©2005 Elsevier Inc.保留所有权利。
Cluster randomization trials are increasingly being used in primary care research. The main feature of these trials is that patients are nested within large clusters such as physician practices or communities and the intervention is applied to the cluster. This study design necessitates calculation of intraclass correlation coefficients in order to determine the required sample size. The purpose of this study is to determine intraclass correlation coefficients for a number of outcome measures at the primary care practice level. The CEART study is a randomized trial testing the effectiveness of translating ATP III guidelines into clinical practice, with primary care physician practices as the unit of randomization and patients as the unit of data collection. The intraclass correlation coefficient (ICC) was < 0.02 and the design effect ranged from 1.0 to 2.3, respectively, for weight, total cholesterol, LDL, non-HDL, glucose, creatinine, and % at non-HDL goal. For smoking status, body mass index, systolic blood pressure, HDL cholesterol triglycerides, total cholesterol/HDL ratio and % at LDL goal, the ICC was 0.02-0.047 and the design effect was 2.6-4.1. The largest ICCs (0.05-0.12) and design effects (4.4-9.4) were found for height and diastolic blood pressure. These findings suggest that cluster randomization may substantially increase the sample size necessary to maintain adequate statistical power for selected outcomes such as diastolic blood pressure studies compared with simple randomization for most outcomes evaluated in this study where the design effect is small to moderate. Overall, the ICCs presented will be useful in calculating sample sizes at the primary care level. © 2005 Elsevier Inc. All rights reserved.