Child Health Evaluative Sciences, The Hospital for Sick Children, 686 Bay Street, Toronto, ON, Canada.
Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON, Canada.
Trials. 2024 Oct 30;25(1):732. doi: 10.1186/s13063-024-08404-2.
Cluster randomized trials (CRTs) are increasingly important for evaluating interventions embedded in health care systems. An essential parameter in sample size calculation to detect both overall and heterogeneous treatment effects for CRTs is the intra-cluster correlation coefficient (ICC) of both outcome and covariates of interest. However, obtaining advance estimates for the ICC can be challenging. When trial outcomes will be obtained from routinely collected data sources, there is an opportunity to obtain reliable ICC estimates in advance of the trial. Using USA national Medicare data, we estimated ICCs for a range of outcomes to inform the design of CRTs for people living with Alzheimer's and related dementias (ADRD).
Data from 2018 Medicare Fee-for-Service beneficiaries, specifically, 1,898,812 individuals (≥ 65 years) with diagnosis of ADRD within 3436 hospital service areas (treated as clusters) and 306 hospital referral regions (treated as fixed strata), were used to calculate unadjusted and adjusted ICC estimates for three outcomes: death, any hospitalizations, and any emergency department (ED) visits and three covariates: age, race and sex. We present both overall and stratum-specific ICC estimates. We illustrate their use in sample size calculations for overall treatment effects as well as detecting treatment effect heterogeneity.
The unadjusted overall ICCs for death, hospitalizations, and ED visits were 0.001, 0.010, and 0.017 respectively. Stratum-specific ICCs varied widely across the 306 HRRs: median 0.001, 0.010 and 0.025 for death, hospitalizations, and ED visits respectively and 0.007, 0.001, and 0.080 for age, sex and race. An interactive R Shiny app is provided that allows users to retrieve estimates overlayed on a map of the USA.
We presented both adjusted and unadjusted ICCs for outcomes as well as unadjusted ICCs for covariates of potential interest from population-level data in the USA and demonstrated how the estimates may be used in sample size calculations for CRTs in ADRD.
集群随机试验 (CRT) 对于评估嵌入医疗保健系统中的干预措施越来越重要。在 CRT 中，检测整体和异质治疗效果的样本量计算的一个基本参数是结果和感兴趣的协变量的组内相关系数 (ICC)。然而，获得 ICC 的预先估计可能具有挑战性。当试验结果将从常规收集的数据来源中获得时，有机会在试验之前获得可靠的 ICC 估计值。我们使用美国国家医疗保险数据，针对一系列结果估计了 ICC，为针对阿尔茨海默病和相关痴呆症 (ADRD) 患者的 CRT 设计提供信息。
使用 2018 年医疗保险费用服务受益人的数据，具体来说，使用 3436 个医院服务区域（视为群组）和 306 个医院转诊区域（视为固定层）内患有 ADRD 的 1898812 名（≥65 岁）个体的未调整和调整后的 ICC 估计值来计算三种结果：死亡、任何住院治疗和任何急诊就诊以及三种协变量：年龄、种族和性别。我们呈现了整体和分层特定的 ICC 估计值。我们展示了它们在整体治疗效果和检测治疗效果异质性的样本量计算中的用途。
死亡、住院治疗和急诊就诊的未调整总体 ICC 分别为 0.001、0.010 和 0.017。306 个 HRR 之间的分层特定 ICC 差异很大：死亡、住院治疗和急诊就诊的中位数分别为 0.001、0.010 和 0.017，年龄、性别和种族的中位数分别为 0.007、0.001 和 0.080。提供了一个交互式 R Shiny 应用程序，允许用户在 USA 的地图上检索叠加的估计值。
我们从美国的人群水平数据中提供了结果的调整和未调整的 ICC 以及潜在感兴趣的协变量的未调整 ICC，并展示了如何在 ADRD 的 CRT 样本量计算中使用这些估计值。
