Industry sponsorship and research outcome: systematic review with meta-analysis
Abstract
Purpose Clinical research is widely sponsored by drug and device companies. We investigated whether industry sponsored drug and device studies have more favorable outcomes and differ in risk of bias, compared with studies having other sources of sponsorship. This review is an update of a previous Cochrane review.Methods In this update we searched MEDLINE and Embase (2010 to February 2015), Cochrane Methodology Register (2015, Issue 2) and Web of Science (June 2015). We included empirical studies that quantitatively compared primary research studies of drugs or medical devices sponsored by industry with studies with other sources of sponsorship. Two assessors included papers, extracted data and assessed risk of bias. Outcomes included favorable results, favorable conclusions, effect size, risk of bias and whether conclusions agreed with results.Results We included 27 additional papers in this update (review now includes 75 papers). Industry sponsored studies more often had favorable efficacy results, RR: 1.27 (95% CI 1.17-1.37), no difference in harms results RR: 1.37 (95% CI 0.64-2.93) and more often favorable conclusions RR: 1.34 (95% CI 1.19-1.51) compared with non-industry sponsored studies. Nineteen papers reported on sponsorship and efficacy effect size, but could not be pooled due to differences in reporting of data and heterogeneity of results. Comparing industry and non-industry sponsored studies, we did not find a difference in risk of bias from sequence generation, allocation concealment, follow-up and selective outcome reporting. However, industry sponsored studies more often had low risk of bias from blinding, RR: 1.25 (95% CI 1.05-1.50), compared with non-industry sponsored studies.Conclusions Drug and device studies sponsored by manufacturing companies have more favorable efficacy results and conclusions than studies sponsored by other sources.
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The source of research funding can lead to bias or perceptions of bias in the design, conduct, or interpretation of research.
“The source of research funding can lead to bias or perceptions of bias in the design, conduct, or interpretation of research.121 122”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [121]
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- Authors
- Lundh, A., Lexchin, J., Mintzes, B., Schroll, J. B., & Bero, L.
- Affiliations
- University of Southern Denmark (DK)
- York University (CA)
- The University of Sydney (AU)
- Hvidovre Hospital (DK)
- Corresponding author
- Lundh, Andreas
- Venue
- Intensive Care Medicine
- Published
- 2018-08-21
- Volume
- 44
- Issue
- 10
- Pages
- 1603-1612
- ISSN
- 0342-4642 (ISSN-L)
- 1432-1238
- OpenAlex
- W2888260605
- Semantic Scholar
- c39013a69577395e9c63ac2e2d7e4e8ac4c36583
- Cited by
- 162
- References
- 58
- Citations by year
- 2026: 12 · 2025: 21 · 2024: 18 · 2023: 21 · 2022: 27 · 2021: 23 · 2020: 23 · 2019: 13 · 2018: 3 · 2017: 1
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Intensive Care Medicine (publishedVersion)
- PubMed (publishedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion)
- Funders
- Macquarie University
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmaceutical industry and healthcare
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Global Public Health Policies and Epidemiology
- Social Sciences › Business, Management and Accounting › Strategy and Management › Political Influence and Corporate Strategies
- Keywords
- industry funding, pharmaceutical industry, medical device industry, research funding sources, research bias, risk of bias, blinding, selective outcome reporting, conflicts of interest, clinical trials
- MeSH terms
- Drug Industry, Equipment and Supplies, Research Support as Topic, Treatment Outcome
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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