Pfaffenlehner Michelle, Behrens Max, Zöller Daniela, Ungethüm Kathrin, Günther Kai, Rücker Viktoria, Reese Jens-Peter, Heuschmann Peter, Kesselmeier Miriam, Remo Flavia, Scherag André, Binder Harald, Binder Nadine
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Freiburg Center for Data Analysis, Modeling and AI, University of Freiburg, Freiburg, Germany.
BMC Med Res Methodol. 2025 Jan 14;25(1):8. doi: 10.1186/s12874-024-02440-x.
The integration of real-world evidence (RWE) from real-world data (RWD) in clinical research is crucial for bridging the gap between clinical trial results and real-world outcomes. Analyzing routinely collected data to generate clinical evidence faces methodological concerns like confounding and bias, similar to prospectively documented observational studies. This study focuses on additional limitations frequently reported in the literature, providing an overview of the challenges and biases inherent to analyzing routine clinical care data, including health claims data (hereafter: routine data).
We conducted a literature search on routine data studies in four high-impact journals based on the Journal Citation Reports (JCR) category "Medicine, General & Internal" as of 2022 and three oncology journals, covering articles published from January 2018 to October 2023. Articles were screened and categorized into three scenarios based on their potential to provide meaningful RWE: (1) Burden of Disease, (2) Safety and Risk Group Analysis, and (3) Treatment Comparison. Limitations of this type of data cited in the discussion sections were extracted and classified according to different bias types: main bias categories in non-randomized studies (information bias, reporting bias, selection bias, confounding) and additional routine data-specific challenges (i.e., operationalization, coding, follow-up, missing data, validation, and data quality). These classifications were then ranked by relevance in a focus group meeting of methodological experts. The search was pre-specified and registered in PROSPERO (CRD42023477616).
In October 2023, 227 articles were identified, 69 were assessed for eligibility, and 39 were included in the review: 11 on the burden of disease, 17 on safety and risk group analysis, and 11 on treatment comparison. Besides typical biases in observational studies, we identified additional challenges specific to RWE frequently mentioned in the discussion sections. The focus group had varied opinions on the limitations of Safety and Risk Group Analysis and Treatment Comparison but agreed on the essential limitations for the Burden of Disease category.
This review provides a comprehensive overview of potential limitations and biases in analyzing routine data reported in recent high-impact journals. We highlighted key challenges that have high potential to impact analysis results, emphasizing the need for thorough consideration and discussion for meaningful inferences.
将来自真实世界数据(RWD)的真实世界证据(RWE)整合到临床研究中,对于弥合临床试验结果与真实世界结局之间的差距至关重要。与前瞻性记录的观察性研究类似,分析常规收集的数据以生成临床证据面临着诸如混杂和偏倚等方法学问题。本研究聚焦于文献中经常报道的其他局限性,概述分析常规临床护理数据(包括健康声明数据,以下简称:常规数据)所固有的挑战和偏倚。
我们在基于2022年《期刊引证报告》(JCR)分类“医学,综合与内科”的四本高影响力期刊以及三本肿瘤学期刊上,对常规数据研究进行了文献检索,涵盖2018年1月至2023年10月发表的文章。根据文章提供有意义的RWE的潜力,对文章进行筛选并分为三种情况:(1)疾病负担,(2)安全性和风险组分析,以及(3)治疗比较。提取讨论部分中引用的这类数据的局限性,并根据不同的偏倚类型进行分类:非随机研究中的主要偏倚类别(信息偏倚、报告偏倚、选择偏倚、混杂)以及常规数据特有的其他挑战(即操作化、编码、随访、缺失数据、验证和数据质量)。然后,在方法学专家的焦点小组会议上,根据相关性对这些分类进行排序。该检索预先指定并在PROSPERO(CRD42023477616)中进行了注册。
2023年10月,共识别出227篇文章,评估了69篇文章的 eligibility,39篇文章纳入综述:11篇关于疾病负担,17篇关于安全性和风险组分析,11篇关于治疗比较。除了观察性研究中的典型偏倚外,我们还在讨论部分中识别出了RWE特有的其他挑战。焦点小组对安全性和风险组分析以及治疗比较的局限性有不同意见,但对疾病负担类别的基本局限性达成了共识。
本综述全面概述了近期高影响力期刊中报道的分析常规数据时潜在的局限性和偏倚。我们强调了对分析结果有高潜在影响的关键挑战,强调了为进行有意义的推断而进行深入考虑和讨论的必要性。