Improving Implementation and QI Research with Regression Risk Analysis

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，当它的结果可以使用公正，直观和明确的措施来报告。不幸的是，当结果是二元的（例如死亡vs.活着，生病vs.健康等）时，最常用的调整混杂的方法是逻辑回归，通常会产生一个调整的优势比，这个测量既不是直观的，也不是更理想的调整风险比的无偏估计。与调整后的风险比相比，调整后的优势比夸大了影响的程度，尤其是在结果并不罕见的情况下。调整后的风险差异往往具有独立的利益。研究人员最近发表了一篇论文，描述了回归风险分析（RRA），这是一种创新的方法，可以让研究人员准确地估计非线性模型（如逻辑回归）的调整风险比和调整风险差异。相对于目前的方法，RRA是一个重大的进步。本项目将扩展RRA，使其更适用于QI研究中经常遇到的研究设计，如复杂的调查样本、聚类、多项回归（即结果在2个以上类别中）和变量之间的相互作用。通过允许根据调整风险比和调整风险差进行逻辑回归报告，RRA将提高研究人员报告直观、可操作和可解释的发现的能力。研究的消费者，如管理者和决策者也将从中受益。本项目将扩展回归风险分析，以增强其与QI研究的相关性，包括：1。从逻辑回归中改进估计风险度量（比率和差异）及其标准误差的方法，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（效应修正）；和多项回归。2. 使用蒙特卡罗模拟验证估计；, 3。编写SAS和STATA代码，并提供教学实例，使典型的卫生服务/质量改进研究人员能够使用这些技术。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。本课题将为QI研究方法的工具箱增添一种重要而有力的方法。在本研究项目结束时，使用逻辑回归的QI研究人员将能够访问回归风险分析，从而提高他们向研究对象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）度量来描述的证据，这些度量是公正和直观的。

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.