Evidence map›Paper›PMID 39810151›Full record

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.

Michelle Pfaffenlehner, Max Behrens, Daniela Zöller, Kathrin Ungethüm, Kai Günther, Viktoria Rücker, Jens-Peter Reese, Peter Heuschmann, Miriam Kesselmeier, Flavia Remo and 4 more

Registry-linked trialAbstract readSystematic Review
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT07513259 completednot on this map

Pre-Diagnosis GLP-1 Receptor Agonist Use and Post-Cancer Mortality in Adults With Obesity and Type 2 Diabetes: A Target Trial Emulation

TypeobservationalSponsorChung Shan Medical UniversityRan2018 to 2025Enrolled203,424ConditionsObesity & Overweight, Diabetes Mellitus Type 2, Neoplasms
3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  18. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Michelle PfaffenlehnerInstitute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany. michelle.pfaffenlehner@uniklinik-freiburg.de.
Max BehrensInstitute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Daniela ZöllerInstitute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Kathrin UngethümInstitute for Medical Data Sciences, University Hospital Würzburg, Würzburg, Germany.
Kai GüntherInstitute for Medical Data Sciences, University Hospital Würzburg, Würzburg, Germany.
Viktoria RückerInstitute for Clinical Epidemiology and Biometry, University Würzburg, Würzburg, Germany.
Jens-Peter ReeseInstitute for Medical Data Sciences, University Hospital Würzburg, Würzburg, Germany.
Peter HeuschmannInstitute for Medical Data Sciences, University Hospital Würzburg, Würzburg, Germany.
Miriam KesselmeierInstitute of Medical Statistics, Computer and Data Sciences, Friedrich Schiller University & Jena University Hospital, Jena, Germany.
Flavia RemoInstitute of Medical Statistics, Computer and Data Sciences, Friedrich Schiller University & Jena University Hospital, Jena, Germany.
André ScheragInstitute of Medical Statistics, Computer and Data Sciences, Friedrich Schiller University & Jena University Hospital, Jena, Germany.
Harald BinderInstitute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Nadine BinderFreiburg Center for Data Analysis, Modeling and AI, University of Freiburg, Freiburg, Germany.
EVA4MII project

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Evidence-Based MedicineResearch DesignBiasHumansBiasEHRLimitationRapid reviewReal-world evidenceRoutine clinical care data

Identifiers

PMID39810151
PMCPMC11731536

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.