Evidence map›Paper›PMID 36335315›Full record

ReviewBMC medical research methodology2022

Real-world data: a brief review of the methods, applications, challenges and opportunities.

Fang Liu, Demosthenes Panagiotakos

Erratum issuedOpen access · goldAbstract readReview
In one paragraph

Review in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 258 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
258citing papers in PubMed, 9 pooled it
41.5field-weighted citation impact, top 1% of its field
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

258 citing papers in PubMed, 9 syntheses or guidelines pooled it, 425 citations in OpenAlex.

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  11. Did You Know That Confounding Could Bias Results Obtained within an Economic Evaluation?Medical decision making : an international journal of the Society for Medical Decision Making · 2026
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198 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 2 countries.

Fang LiuDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame, 46530, Notre Dame, IN, USA. fliu2@nd.edu.ORCID http://orcid.org/0000-0003-3028-5927
Demosthenes PanagiotakosSchool of Health Sciences and Education, Harokopio University, Athens, Greece.
Harokopio University of Athens · GRUniversity of Notre Dame · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe increased adoption of the internet, social media, wearable devices, e-health services, and other technology-driven services in medicine and healthcare has led to the rapid generation of various types of digital data, providing a valuable data source beyond the confines of traditional clinical trials, epidemiological studies, and lab-based experiments.

methodsWe provide a brief overview on the type and sources of real-world data and the common models and approaches to utilize and analyze real-world data. We discuss the challenges and opportunities of using real-world data for evidence-based decision making This review does not aim to be comprehensive or cover all aspects of the intriguing topic on RWD (from both the research and practical perspectives) but serves as a primer and provides useful sources for readers who interested in this topic. RESULTS AND

conclusionsReal-world hold great potential for generating real-world evidence for designing and conducting confirmatory trials and answering questions that may not be addressed otherwise. The voluminosity and complexity of real-world data also call for development of more appropriate, sophisticated, and innovative data processing and analysis techniques while maintaining scientific rigor in research findings, and attentions to data ethics to harness the power of real-world data.

Indexed as

Delivery of Health CareInformation Storage and RetrievalHumansArtificial intelligenceCausal inferenceElectronic health recordsMachine learningReal-world data (RWD)Real-world evidence (RWE)

Identifiers

PMID36335315
PMCPMC9636688
OpenAlexW4308292299

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.