This study introduces recent advances in statistical power analysis methods and tools for designing and analyzing randomized cost-effectiveness trials (RCETs) to evaluate the causal effects and costs of social work interventions. The article focuses on two-level designs, where, for example, students are nested within schools, with interventions applied either at the school level (cluster design) or student level (multisite design). We explore three statistical modeling strategies—random-effects, constant-effects, and fixed-effects models—to assess the cost-effectiveness of interventions, and we develop corresponding power analysis methods and tools. Power is influenced by effect size, sample sizes, and design parameters. We developed a user-friendly tool, PowerUp!-CEA, to aid researchers in planning RCETs. When designing RCETs, it is crucial to consider cost variance, its nested effects, and the covariance between effectiveness and cost data, as neglecting these factors may lead to underestimated power.

中文翻译：

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两级随机成本效益试验设计中的样本量规划


本研究介绍了统计功效分析方法和工具的最新进展，用于设计和分析随机成本效益试验 （RCET），以评估社会工作干预的因果效应和成本。本文重点介绍两级设计，例如，学生嵌套在学校内，干预应用于学校级别（集群设计）或学生级别（多站点设计）。我们探索了三种统计建模策略——随机效应、常效应和固定效应模型——来评估干预措施的成本效益，并开发了相应的功效分析方法和工具。功效受效应大小、样本量和设计参数的影响。我们开发了一个用户友好的工具 PowerUp！-CEA，以帮助研究人员规划 RCET。在设计 RCET 时，考虑成本方差、其嵌套效应以及有效性和成本数据之间的协方差至关重要，因为忽视这些因素可能会导致低估功率。