SynthesisBMC medical research methodology2025
Methodological challenges using routine clinical care data for real-world evidence: a rapid review utilizing a systematic literature search and focus group discussion.
Synthesis in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07513259 (Pre-Diagnosis GLP-1 Receptor Agonist Use and Post-Cancer Mortality in Adults With Obesity and Type 2 Diabetes), which is not on this map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Pre-Diagnosis GLP-1 Receptor Agonist Use and Post-Cancer Mortality in Adults With Obesity and Type 2 Diabetes: A Target Trial Emulation
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Assessing cost-effectiveness in oncology treatment sequences: a review of pathway modeling methods for health technology assessment.International journal of technology assessment in health care · 2026Review
- Representativeness and external comparability of the InGef research database for epidemiological studies in Germany.BMC health services research · 2026Article
- [Evidence generation and methodological consultation by the project "EVAluation research based on data from routine clinical care 4 the Medical Informatics Initiative" (EVA4MII)].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2026Review
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- Adult SMA REACH: A UK clinical network and real-world data collection study for adults living with spinal muscular atrophy.Journal of neuromuscular diseases · 2026Article
- Real-world evidence in metabolic dysfunction-associated steatotic liver disease (MASLD): insights, challenges, and future directions.The Lancet regional health. Europe · 2026Review
- A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review.Journal of biomedical informatics · 2026Article
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- What to Expect in 2026-2027: Important Health Economics and Outcomes Research Topics.ClinicoEconomics and outcomes research : CEOR · 2026Article
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- Clinical endpoints in pragmatic heart failure trials: From data collection to clinical endpoint classification.ESC heart failure · 2025Review
- Data Missingness and Equity Implications in the Nation's Largest Student Fitness Surveillance System: The New York City School Based Physical Fitness Testing Programs, 2006-2020.The Journal of school health · 2025Observational
- Harnessing digital health technologies and real-world evidence to enhance clinical research and patient outcomes.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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).
methodsWe 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).
resultsIn 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.
conclusionThis 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.