Sinnappah Klarissa A, Hughes Dyfrig A, Stocker Sophie L, Wright Daniel F B
School of Pharmacy, University of Otago, Dunedin, New Zealand.
Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK.
Br J Clin Pharmacol. 2025 May;91(5):1457-1478. doi: 10.1111/bcp.16382. Epub 2025 Jan 21.
An unbiased means of documenting medication-taking is important to ensure quality evidence about adherence research and to accurately identify individuals at risk of suboptimal adherence for the development of targeted and effective interventions. Guidance to assist researchers in the understanding of risk of bias when conducting or reviewing adherence research is currently not available. To address this gap, tools to identify and gauge the magnitude of important biases that may impact adherence research have been developed.
The Risk of Bias tool for Interventional Adherence Studies (RoBIAS) and the Risk of Bias tool for Observational Adherence Studies (RoBOAS) were constructed from a literature review of key adherence guidelines/frameworks, drafted initially through author consensus. The draft bias tools were piloted and evaluated with expert adherence researchers through an online survey platform to assess the internal consistency and agreement in responses, including gather "free text" feedback to improve the tool's utility.
Of the 121 approached reviewers, only 20 out of the 30 reviewers who consented to participate completed the piloting of the tools. Both tools are structured around four domains relating to: (i) study design, (ii) randomization (RoBIAS tool) and confounding factors (RoBOAS tool), (iii) adherence outcome measurement, and (iv) data analysis. Each domain consists of items/statements, mapped to specific biases relevant to adherence research and study designs, including a domain-based ranking scale to determine the appropriate risk of bias judgement.
The tools are intended to have utility when systematically reviewing adherence research and to inform the design of future adherence studies.
记录用药情况的无偏倚方法对于确保关于依从性研究的高质量证据以及准确识别依从性欠佳风险个体以制定有针对性且有效的干预措施至关重要。目前尚无指导意见协助研究人员在开展或审查依从性研究时理解偏倚风险。为填补这一空白，已开发出用于识别和衡量可能影响依从性研究的重要偏倚程度的工具。
干预性依从性研究偏倚工具（RoBIAS）和观察性依从性研究偏倚工具（RoBOAS）是通过对关键依从性指南/框架的文献综述构建而成，最初由作者达成共识起草。通过在线调查平台对偏倚工具草案进行试点并由依从性研究专家进行评估，以评估内部一致性和回答的一致性，包括收集“自由文本”反馈以提高工具的实用性。
在121名被邀请的评审人员中，同意参与的30名评审人员中只有20名完成了工具的试点。这两个工具均围绕四个领域构建，分别涉及：（i）研究设计，（ii）随机化（RoBIAS工具）和混杂因素（RoBOAS工具），（iii）依从性结果测量，以及（iv）数据分析。每个领域由项目/陈述组成，对应与依从性研究和研究设计相关的特定偏倚，包括一个基于领域的等级量表，以确定适当的偏倚风险判断。
这些工具旨在在系统审查依从性研究时发挥作用，并为未来依从性研究的设计提供参考。
