Generating Accurate Estimates of Required Sample Size for Multilevel Implementation Studies in Mental Health

生成心理健康多层次实施研究所需样本量的准确估计

• 批准号: 10188231
• 负责人: Nathaniel J. Williams
• 金额: $ 30.3万
• 依托单位: BOISE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-11 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10188231
• 关键词: Accounting; Address; Adoption; American; Caring; Characteristics; Clinical; Communities; Data Analyses; Data Set; Foundations; Funding; Future; Health; Individual; Intervention; Literature; Manuscripts; Measurement; Mental Health; Mental disorders; Methodology; Methods; Mission; National Institute of Mental Health; Outcome; Partner in relationship; Patient Care; Patient-Focused Outcomes; Patients; Personal Satisfaction; Pilot Projects; Population; Predictive Factor; Process; Provider; Publishing; Reference Values; Reporter; Research; Research Design; Research Personnel; Resources; Risk; Sample Size; Sampling; Science; Scientist; Speed; System; Testing; Translations; United States National Institutes of Health; Work; behavioral health; community setting; cost; design; evidence base; experience; health care quality; health care settings; implementation design; implementation determinants; implementation research; implementation science; implementation strategy; implementation study; improved; interest; patient population; power analysis; predictive modeling; prevent; study characteristics; study population; tool

Project Summary This project fills a major methods gap that prevents investigators from designing studies with accurate esti- mates of required sample size for multilevel behavioral health implementation studies. Implementation science is essential to achieving NIMH’s mission. An essential step in designing implementation studies is to conduct a statistical power analysis to determine the minimum sample size required to statistically detect effects of interest. Power analyses for implementation research are more complicated because they need to account for (a) patients nested within providers who are nested within organizations or other systems, and (b) scientific aims that typically focus on testing (or at a minimum accounting for) cross-level effects of higher-level (e.g., organization, clinician) implementation determinants or strategies on lower-level (e.g., patient) outcomes. While multilevel power anal- ysis tools are available to accommodate these types of nested studies, the tools require investigators to have prior estimates of three key design parameters to determine the proper sample size for their study —intraclass correlation coefficient (ICC), effect size, and proportion of variance explained by covariates—which are not rou- tinely available from the published literature and cannot be reliably estimated from small pilot studies. Power analyses that use inaccurate estimates of these design parameters are highly likely to be either underpowered, and consequently at-risk of not detecting important effects, or over-powered, and consequently wasteful of lim- ited resources. Lack of reference values for these parameters is a foundational barrier to the field because even small changes in design parameters can dramatically alter the effective sample size from N=300 to N=50. NIMH has funded a large number of implementation studies during the last 10 years (N=140) which provides an opportunity for us to re-access the datasets from these projects to generate accurate estimates of multilevel design parameters for behavioral health implementation studies. We will use NIH-RePorter to identify all NIMH- funded behavioral health implementation studies conducted during the last 10 years and collaborate with PIs to extract design parameters for targeted implementation and clinical outcomes, which we will summarize and pub- lish for the field. We will also generate a predictive model that enables PIs to estimate design parameters tailored to the characteristics of their new studies. Building on our preliminary work within the Penn NIMH ALACRITY Center, this project will (1) generate pooled estimates and ranges of design parameters (i.e., ICCs, effect sizes, covariate R2) needed to accurately estimate sample size in multilevel behavioral health implementation studies, and (2) identify the study characteristics that predict the magnitude of these design parameters. Completion of this work will remove a ubiquitous methodological barrier that undermines the advancement of implementation science in behavioral health. The study will contribute to higher quality, more replicable science, more efficient use of NIMH resources, and higher impact implementation research to improve healthcare quality and well-being for millions of individuals who experience psychiatric disorders each year.

项目摘要 这个项目填补了一个主要的方法空白，使研究人员无法设计具有准确估计的研究。 多水平行为健康实施研究所需样本量的配对。实施科学 对于完成NIMH的使命是至关重要的。设计实施研究的一个基本步骤是进行 统计功率分析，以确定统计检测感兴趣的影响所需的最小样本量。 实施研究的功率分析更为复杂，因为它们需要考虑(A)患者 嵌套在嵌套在组织或其他系统内的提供者内，以及(B)通常 专注于测试(或至少说明)更高级别(例如，组织、临床医生)的跨级别影响 较低级别(例如，患者)结果的实施决定因素或战略。而多电平功率肛门- 可以使用解决工具来适应这些类型的嵌套研究，这些工具要求调查人员具有 预先估计三个关键设计参数，以确定其研究的适当样本量-组内 相关系数(ICC)、效应大小和由协变量解释的方差比例。 从已出版的文献中可以得到很小的数据，而不能从小规模的试点研究中可靠地估计。电源 使用对这些设计参数的不准确估计的分析很可能要么动力不足， 因此有可能检测不到重要的影响，或功率过大，从而浪费LIM- 有限的资源。缺乏这些参数的参考值是该领域的一个基本障碍，因为即使 设计参数的微小变化可以极大地改变有效样本量，从N=300到N=50。 NIMH在过去10年(N=140)资助了大量的实施研究，提供了 我们有机会重新访问这些项目的数据集，以生成多水平的准确估计 为行为健康实施研究设计参数。我们将使用NIH-Report来识别所有NIMH- 资助过去10年进行的行为健康实施研究，并与PI合作 为有针对性的实施和临床结果提取设计参数，我们将总结并公布- 在球场上学英语。我们还将生成一个预测模型，使PI能够估计定制的设计参数 以适应他们新研究的特点。以我们在宾夕法尼亚州立大学的前期工作为基础 中心，该项目将(1)生成综合估计和设计参数范围(即ICC、效果大小、 协变量R2)在多水平行为健康实施研究中准确估计样本大小所需的， 以及(2)确定预测这些设计参数大小的研究特征。完成 这项工作将消除无处不在的方法障碍，这一障碍破坏了执行工作的进展 行为健康方面的科学。这项研究将有助于更高质量、更可复制的科学、更高效 使用NIMH资源，以及更有影响力的实施研究，以改善医疗质量和福祉 对于每年经历精神障碍的数百万人来说。