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

随机对照试验通常有二元主要结果，但分析二元结果的方法尚在研究中。CONSORT报告随机试验结果的指南建议，对于二元结果，研究人员报告相对和绝对的效果度量。此外，在报告效果的相对度量时，由于优势比经常被误解为风险比，并且由于风险比和优势比随着结果的普遍性变得越来越不常见而偏离，相对风险可以作为相对尺度上量化效果的首选度量。虽然相对风险和风险差异的计算相对直接，不需要对任何协变量进行调整，但在实践中，协变量调整通常是必要的，要么是因为机会不平衡，要么是为了提高治疗效果的精度。在调整协变量的同时计算相对风险和风险差的方法更加复杂，并且增加了模型不收敛的可能性。在实践中，研究人员经常不能报告效果的绝对度量，而在报告效果的相对度量时使用优势比。这可能是由于多种原因造成的，但造成这种情况的因素包括方法的复杂性。聚类随机试验中的协变量调整：在聚类随机试验（crt）中，协变量调整可能很重要的原因有很多。首先，在crt中，随机化是在集群水平上进行的，个体可能在随机化后被招募，这意味着可能存在“选择偏差”（称为招募或识别偏差）的重要风险[Eldridge 2008]。这些表现为治疗组和对照组个体特征的差异——在极端情况下，可能使试验更像是一项观察性研究[Bolzern 2018；2021年复活节]。因此，在crt中，协变量调整可以起到防止混杂的重要作用[Leyrat 2014]。此外，在个体随机化下，即使选择偏倚的风险可以忽略不计（例如，在随机化前招募的研究中），协变量调整仍可能提高统计精度[Li 2016；李2017]。然而，在聚类试验中，协变量调整有许多细微差别。例如，在crt中，协变量可以在集群或个体或两者的水平上测量；因此，可以围绕如何包含协变量做出重要决定[Begg 2003]。例如，集群大小是一个典型的分层因素，通常使用集群大小的历史度量来实现。在分析阶段，可能会出现关于分析中应该调整哪些协变量的问题：分类历史版本；或者更现代的连续版本。此外，有许多方法可以接近crt中的协变量调整，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（也简称为标准化或g计算）的直接协变量调整[Benkeset 2021；Morris 2022]，或聚类水平分析。研究内容：目的：在估计相对风险和风险差异时，建立聚类随机试验中协变量调整的方法学框架。文献回顾及个案研究。回顾在crt中用于二元结果的协变量调整的报告和分析方法。该项目将系统地回顾和记录在报告二元结果时，crt中协变量调整的当代方法-考虑如何选择协变量纳入；是否在集群或个人层面进行调整；以及分析方法。这将通知wp 1和wp 2。b.确定几个案例研究来说明imp的方法

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