Covariate adjustment in cluster randomised trials with binar y outcomes focussing on relative risks and risk difference

整群随机试验中的协变量调整，二元结果侧重于相对风险和风险差异

• 批准号: 2893574
• 金额: --
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2893574
• 关键词: Covariate; adjustment; cluster; randomised; trials

Randomised controlled trials frequently have binary primary outcomes yet methods for the analysis of binary outcomes is under researched. The CONSORT guideline for reporting of results from randomised trials recommends that for binary outcomes researchers report both relative and absolute measures of effect. Furthermore, when reporting relative measures of effect, because odds ratios are often misinterpreted as risk ratios and because risk ratios and odds ratios diverge as the prevalence of the outcome becomes increasingly less common, relative risks can be the preferred measure of quantifying effects on the relative scale. Whilst relative risks and risk differences are relatively straight forward to calculate without adjustment for any covariates, in practice covariate adjustment is often warranted either because of chance imbalance or to improve precision of the treatment effect. Methods to compute relative risks and risk difference whilst adjusting for covariates are more complex and have increased possibility of model non-convergence. In practice, researchers often fail to report absolute measures of effect and when reporting relative measures of effect do so using odds ratios. This is likely due to a multitude of reasons, but contributing factors include complexity of methods.Covariate adjustment in cluster randomised trials: There are a number of reasons why covariate adjustment is likely to be important in cluster randomised trials (CRTs). Firstly, in CRTs randomisation is at the level of the cluster, and individuals might be recruited after randomisation, meaning there can be important risks of "selection bias" (known as recruitment or identification biases) [Eldridge 2008]. These manifest as differences in the characteristics of individuals under treatment and control arms - and in extreme cases can render the trial more like an observational study [Bolzern 2018; Easter 2021]. Thus, in CRTs, covariate adjustment can have an important role of protecting against confounding [Leyrat 2014]. Furthermore, as under individual randomisation, even when the risk of selection bias is negligible (e.g., in studies with pre-randomisation recruitment), covariate adjustment might still improve statistical precision [Li 2016; Li 2017]. However, there are many nuances around covariate adjustment in cluster trials. For example, in CRTs, covariates might be measured at the level of the cluster, or individual, or both; and there can thus be important decisions around how the covariates are to be included [Begg 2003]. For example, cluster size is a typical stratification factor, often implemented using an historical measure of cluster size. At the analysis stage, questions can arise around what covariate should be adjusted in the analysis: the categorized historical version; or a more contemporary continuous version. Moreover, there are numerous ways to approach covariate adjustment in CRTs, including direct covariate adjustment using generalised linear mixed models or generalised estimating equations, propensity score approaches and marginal standardisation (also known simply as standardisation or G-computation) [Benkeset 2021; Morris 2022], or cluster-level analysis. What the studentship will encompassObjective: To establish a methodological framework for covariate adjustment in cluster randomised trials when estimating relative risks and risk differencesPlans for project:1. Review of the literature and case studya. Review of reporting and analytical approaches for covariate adjustment in CRTs for binary outcomes. This project will systematically review and document contemporary approaches to covariate adjustment in CRTs when reporting binary outcomes- considering how covariates are selected for inclusion; whether they are adjusted for at the level of the cluster or individual; as well as analytical approaches. This will inform WPs 1 and 2. b. Identification of several case studies to illustrate the methodology to imp

随机对照试验经常具有二元主要结果，但正在研究进行二元结果分析的方法。根据随机试验报告结果的CONSORT指南建议，对于二进制结果，研究人员报告了相对和绝对效果的措施。此外，当报告相对效果的相对测量值时，由于几率通常被误解为风险比率，而且风险比和优势比的差异差异，因为结果的流行越来越少，因此相对风险可以成为对相对规模量化效应的首选衡量标准。尽管相对风险和风险差异相对直接进行计算，而无需调整任何协变量，但实际上，由于偶然的失衡或提高治疗效果的精度，因此通常需要进行协变量调整。计算相对风险和风险差异的方法，而对协变量进行调整更为复杂，并且具有不可连接模型的可能性。在实践中，研究人员通常无法报告效果的绝对度量，并且在报告相对效果测量时确实使用赔率比。这可能是由于多种原因，但促成因素包括方法的复杂性。在群集随机试验中，可转化的调整：有很多原因导致协方差调整可能在群集随机试验（CRT）中很重要。首先，在CRT中，随机化处于群集的水平，并且在随机化后可能会招募个体，这意味着可能存在“选择偏见”（称为募集或识别偏见）的重要风险[Eldridge 2008]。这些表现为在治疗和控制臂下的个体特征的差异 - 在极端情况下，可以使试验更像是一项观察性研究[Bolzern 2018;复活节2021]。因此，在CRT中，协变量调整可以具有保护混淆的重要作用[Leyrat 2014]。此外，正如在个人随机化下一样，即使选择偏见的风险可以忽略不计（例如，在随机募集前的研究中），协变量调整仍可能提高统计精度[Li 2016;李2017]。但是，在集群试验中，协变量调整有许多细微差别。例如，在CRT中，可以在群集或个体或两者的水平上测量协变量。因此，围绕如何包括协变量[Begg 2003]，可以做出重要的决定。例如，群集大小是典型的分层因子，通常使用群集大小的历史度量实现。在分析阶段，在分析中应调整哪些协变量：分类的历史版本可能会出现问题；或更现代的连续版本。此外，有多种方法可以在CRT中进行协方差调整，包括使用广义线性混合模型或广义估计方程，倾向得分方法和边际标准化（也仅称为标准化或G型）的直接协变量调整[Benkeset 2021;莫里斯2022]或集群级分析。学生将涵盖的内容：在估计项目的相对风险和风险差异时，为群集随机试验中的协变量调整建立方法学框架：1。文献和案例研究的评论。回顾CRT中二元结果的协变量调整的报告和分析方法。该项目将在报告二进制结果时系统地审查并记录CRT中的协变量调整的当代方法 - 考虑如何选择协变量以包含在内；无论是在集群还是个体的水平上进行调整；以及分析方法。这将告知WPS 1和2。b。识别几个案例研究以说明方法的方法