Improving Implementation and QI Research with Regression Risk Analysis

通过回归风险分析改进实施和 QI 研究

• 批准号: 7848505
• 负责人: Lawrence C Kleinman
• 金额: $ 29.83万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7848505
• 关键词: Improving; Implementation; QI; Research; Regression

DESCRIPTION (provided by applicant): Quality Improvement and Implementation Research (QI Research) is most valuable when its results can be reported using measures that are unbiased, intuitive and unambiguous. Unfortunately logistic regression, the most common method for adjusting for confounding when outcomes are binary (e.g. dead vs. alive, sick vs. healthy, etc) generally yields an adjusted odds ratio, a measure that is neither intuitive nor an unbiased estimator of the more desirable adjusted risk ratio. The adjusted odds ratio overstates the magnitude of impact compared to an adjusted risk ratio, especially when outcomes are not rare. Often the adjusted risk difference would be of independent interest. The study investigators have recently published a paper describing Regression Risk Analysis (RRA), an innovative approach that allows researchers to accurately estimate adjusted risk ratios and adjusted risk differences from nonlinear models like logistic regression. RRA represents a significant advance over current methods. This project will extend RRA to make it more useful for study designs frequently encountered in QI research, such as complex survey samples, clustering, multinomial regression (i.e., outcomes are in more than 2 categories), and interactions between variables. By allowing logistic regression to be reported in terms of the adjusted risk ratio and the adjusted risk difference, RRA will enhance researchers' capacity to report findings that are intuitive, actionable, and interpretable. Consumers of research, such as administrators and policy makers will also benefit. This project will extend regression risk analysis to enhance its relevance for QI research, including to: 1. Refine the method of estimating risk measures (ratios and differences) and their standard errors from logistic regression to account for: Complex sample designs, including stratification, clustering, and equal or disproportionate weighting; Interactions (effect modification) between variables; and Multinomial regression. 2. Validate the estimates using Monte Carlo simulations; and, 3. Develop both SAS and STATA code with teaching examples to make these techniques accessible to typical health services/quality improvement researchers. We will use the internet to make these methods and our computer code widely available. This project will add a significant and powerful method to the tool box of QI research methods. At the conclusion of this research project, QI researchers who use logistic regression will have access to regression risk analysis, improving their capacity to communicate interpretable and actionable results to the consumers of their research. In turn, translation of research will benefit from evidence being described in terms of both relative (risk ratio), and absolute (risk difference) measures that are unbiased and intuitive. PUBLIC HEALTH RELEVANCE: The applicants recently described how to improve logistic regression by estimating two unbiased and intuitive measures, adjusted risk ratio (ARR) and adjusted risk difference (ARD), instead of the less intuitive adjusted odds ratio, which always exaggerates effect size. They intend to extend their method to handle complex survey design, clusters, multinomial outcomes, and interactions, each of which is often encountered in real-world implementation and quality improvement research. They will develop and validate these extensions, and create and disseminate user-friendly software, thus enhancing the capacity of QI researchers to translate their results.

描述（由申请人提供）：质量改进和实施研究（QI研究）是最有价值的，当其结果可以使用无偏见，直观和明确的措施报告。不幸的是，逻辑回归，当结果是二元的（例如死亡与存活，患病与健康等）时，用于调整混杂因素的最常见方法通常会产生调整后的比值比，这是一种既不直观也不是更理想的调整后风险比的无偏估计值的度量。与调整后的风险比相比，调整后的比值比夸大了影响的程度，特别是当结果并不罕见时。调整后的风险差异往往是独立的利益。研究人员最近发表了一篇论文，描述了回归风险分析（RRA），这是一种创新方法，允许研究人员准确估计调整后的风险比和调整后的风险差异，如逻辑回归等非线性模型。RRA代表了对当前方法的重大进步。该项目将扩展RRA，使其对QI研究中经常遇到的研究设计更有用，例如复杂的调查样本，聚类，多项式回归（即，结果属于两个以上类别），以及变量之间的相互作用。通过允许logistic回归报告调整后的风险比和调整后的风险差异，RRA将提高研究人员报告直观，可操作和可解释的结果的能力。研究的消费者，如管理人员和政策制定者也将受益。这个项目将扩展回归风险分析，以提高其相关性的QI研究，包括：1。改进估计风险度量（比率和差异）及其标准误的方法，以考虑：复杂的样本设计，包括分层，聚类和相等或不成比例的权重;变量之间的相互作用（效应修改）;和多项式回归。2.使用Monte Carlo模拟对估计值进行验证;以及，3。开发SAS和STATA代码与教学实例，使这些技术访问典型的卫生服务/质量改进研究人员。我们将利用互联网使这些方法和我们的计算机代码广泛可用。这个项目将为QI研究方法的工具箱增加一个重要而强大的方法。在本研究项目结束时，使用逻辑回归的QI研究人员将可以使用回归风险分析，提高他们向研究消费者传达可解释和可操作结果的能力。反过来，研究的翻译将受益于以相对（风险比）和绝对（风险差异）衡量标准描述的证据，这些衡量标准是公正和直观的。 公共卫生相关性：申请人最近描述了如何通过估计两个无偏和直观的测量值，即调整后的风险比（ARR）和调整后的风险差（ARD），而不是不太直观的调整后的比值比来改善逻辑回归，调整后的比值比总是夸大效应大小。他们打算扩展他们的方法来处理复杂的调查设计，集群，多项结果和相互作用，其中每一个都经常在现实世界的实施和质量改进研究中遇到。他们将开发和验证这些扩展，并创建和传播用户友好的软件，从而提高QI研究人员翻译其结果的能力。