Williams Nathaniel J, Cardamone Nicholas C, Beidas Rinad S, Marcus Steven C
Institute for the Study of Behavioral Health and Addiction, Boise State University, Boise, ID, USA.
School of Social Work, Boise State University, Boise, ID, USA.
Implement Res Pract. 2024 Sep 26;5:26334895241279153. doi: 10.1177/26334895241279153. eCollection 2024 Jan-Dec.
Despite the ubiquity of multilevel sampling, design, and analysis in mental health implementation trials, few resources are available that provide reference values of design parameters (e.g., effect size, intraclass correlation coefficient [ICC], and proportion of variance explained by covariates [covariate ]) needed to accurately determine sample size. The aim of this study was to provide empirical reference values for these parameters by aggregating data on implementation and clinical outcomes from multilevel implementation trials, including cluster randomized trials and individually randomized repeated measures trials, in mental health. The compendium of design parameters presented here represents plausible values that implementation scientists can use to guide sample size calculations for future trials.
We searched NIH RePORTER for all federally funded, multilevel implementation trials addressing mental health populations and settings from 2010 to 2020. For all continuous and binary implementation and clinical outcomes included in eligible trials, we generated values of effect size, ICC, and covariate at each level via secondary analysis of trial data or via extraction of estimates from analyses in published research reports. Effect sizes were calculated as Cohen ; ICCs were generated via one-way random effects ANOVAs; covariate estimates were calculated using the reduction in variance approach.
Seventeen trials were eligible, reporting on 53 implementation and clinical outcomes and 81 contrasts between implementation conditions. Tables of effect size, ICC, and covariate are provided to guide implementation researchers in power analyses for designing multilevel implementation trials in mental health settings, including two- and three-level cluster randomized designs and unit-randomized repeated-measures designs.
Researchers can use the empirical reference values reported in this study to develop meaningful sample size determinations for multilevel implementation trials in mental health. Discussion focuses on the application of the reference values reported in this study.
尽管多级抽样、设计和分析在心理健康实施试验中无处不在,但几乎没有资源可提供准确确定样本量所需的设计参数参考值(例如效应量、组内相关系数[ICC]以及协变量解释的方差比例[协变量])。本研究的目的是通过汇总心理健康领域多级实施试验(包括整群随机试验和个体随机重复测量试验)的实施和临床结果数据,为这些参数提供实证参考值。此处呈现的设计参数汇编代表了实施科学家可用于指导未来试验样本量计算的合理值。
我们在NIH RePORTER中搜索了2010年至2020年期间所有由联邦资助的、针对心理健康人群和环境的多级实施试验。对于符合条件的试验中包含的所有连续和二元实施及临床结果,我们通过对试验数据的二次分析或从已发表研究报告的分析中提取估计值,在每个层面生成效应量、ICC和协变量的值。效应量按照科恩d值计算;ICC通过单向随机效应方差分析生成;协变量估计值使用方差缩减法计算。
17项试验符合条件,报告了53项实施和临床结果以及81种实施条件之间的对比。提供了效应量、ICC和协变量的表格,以指导实施研究人员在心理健康环境中设计多级实施试验的功效分析,包括两级和三级整群随机设计以及单位随机重复测量设计。
研究人员可以使用本研究报告的实证参考值,为心理健康领域的多级实施试验确定有意义的样本量。讨论集中在本研究报告的参考值的应用上。