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Industry sponsorship and research outcome
Open accessAbstract
BACKGROUND: Clinical research affecting how doctors practice medicine is increasingly sponsored by companies that make drugs and medical devices. Previous systematic reviews have found that pharmaceutical-industry sponsored studies are more often favorable to the sponsor's product compared with studies with other sources of sponsorship. A similar association between sponsorship and outcomes have been found for device studies, but the body of evidence is not as strong as for sponsorship of drug studies. This review is an update of a previous Cochrane review and includes empirical studies on the association between sponsorship and research outcome. OBJECTIVES: To investigate 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. SEARCH METHODS: In this update we searched MEDLINE (2010 to February 2015), Embase (2010 to February 2015), the Cochrane Methodology Register (2015, Issue 2) and Web of Science (June 2015). In addition, we searched reference lists of included papers, previous systematic reviews and author files. SELECTION CRITERIA: Cross-sectional studies, cohort studies, systematic reviews and meta-analyses that quantitatively compared primary research studies of drugs or medical devices sponsored by industry with studies with other sources of sponsorship. We had no language restrictions. DATA COLLECTION AND ANALYSIS: Two assessors screened abstracts and identified and included relevant papers. Two assessors extracted data, and we contacted authors of included papers for additional unpublished data. Outcomes included favorable results, favorable conclusions, effect size, risk of bias and whether the conclusions agreed with the study results. Two assessors assessed risk of bias of included papers. We calculated pooled risk ratios (RR) for dichotomous data (with 95% confidence intervals (CIs)). MAIN RESULTS: Twenty-seven new papers were included in this update and in total the review contains 75 included papers. Industry sponsored studies more often had favorable efficacy results, RR: 1.27 (95% CI: 1.17 to 1.37) (25 papers) (moderate quality evidence), similar harms results RR: 1.37 (95% CI: 0.64 to 2.93) (four papers) (very low quality evidence) and more often favorable conclusions RR: 1.34 (95% CI: 1.19 to 1.51) (29 papers) (low quality evidence) compared with non-industry sponsored studies. Nineteen papers reported on sponsorship and efficacy effect size, but could not be pooled due to differences in their reporting of data and the results were heterogeneous. We did not find a difference between drug and device studies in the association between sponsorship and conclusions (test for interaction, P = 0.98) (four papers). 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 to 1.50) (13 papers), compared with non-industry sponsored studies. In industry sponsored studies, there was less agreement between the results and the conclusions than in non-industry sponsored studies, RR: 0.83 (95% CI: 0.70 to 0.98) (six papers). AUTHORS' CONCLUSIONS: Sponsorship of drug and device studies by the manufacturing company leads to more favorable efficacy results and conclusions than sponsorship by other sources. Our analyses suggest the existence of an industry bias that cannot be explained by standard 'Risk of bias' assessments.
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“There is potential for bias in the review findings arising from such involvement, particularly when the funder or sponsor has an interest in obtaining a particular result.157”
From PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews (2021) · cites this paper as [157]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“We did not include domains for features that would be expected to operate indirectly, through the included bias domains. 14 15”
From RoB 2: a revised tool for assessing risk of bias in randomised trials (2019) · cites this paper as [14]
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“The interference of industry bias is likely minimal, in contrast to other subject areas [22,23], given the lack of profits available for CoQ10.”
From Effect of Coenzyme Q10 on statin-associated myalgia and adherence to statin therapy: A systematic review and meta-analysis (2020) · cites this paper as [23]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Reported mechanisms of sponsorship bias include eligibility criteria, sample size, allocation and blinding, choice of comparators, outcome assessment, statistical analyses and even practices, such as selective outcome reporting (Lundh et al. 2017; Holman and Elliott 2018; Catalogue of Bias 2019).”
From Sponsorship bias in randomised controlled trials on postoperative pain after third molar extraction: A meta-research study (2026) · cites this paper as Lundh et al. 2017
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“Concerns have been raised that industry sponsorship may bias clinical research, particularly RCTs.”
From Sponsorship bias in randomised controlled trials on postoperative pain after third molar extraction: A meta-research study (2026) · cites this paper as Lundh et al. 2017
In the citing paper
“Although both industry funded and non-industry funded trials are susceptible to bias, industry funding is associated with conclusions that favour the intervention.”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [106]
In the citing paper
“A systematic review of 75 methodological studies, comparing industry funded with non-industry funded studies (mostly randomised trials), reported that industry studies more often had favourable conclusions (risk ratio 1.34 (95% CI 1.19 to 1.51) compared with non‐industry studies.106”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [106]
In the citing paper
“Lundh et al 39 showed that industry involvement by the manufacturing company leads to more favourable results and conclusions than sponsorship by other sources.”
From Should antidepressants be used for major depressive disorder? (2020) · cites this paper as [39]
In the citing paper
“As a substantial proportion of research concerning antidepressants is prone to ‘for profit bias’, the results of previous results likely overestimate benefit and underestimate harm.”
From Should antidepressants be used for major depressive disorder? (2020) · cites this paper as [39]
In the citing paper
“Industry-sponsored studies showed more often benefits, relative risk (RR) 1.27 (95% CI 1.17 to 1.37), and more often favourable conclusions, RR 1.34 (95% CI 1.19 to 1.51).”
From Should antidepressants be used for major depressive disorder? (2020) · cites this paper as [39]
In the citing paper
“Trials at high or unclear risk of bias tend to overestimate benefits and underestimate harms.”
From Should antidepressants be used for major depressive disorder? (2020) · cites this paper as [39]
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“In this regard, a Cochrane review that included 75 articles compared favorable outcomes between industry‐funded studies and studies using other funding sources.”
From The Cochrane risk of bias assessment tool 2 (RoB 2) versus the original RoB: A perspective on the pros and cons (2024) · cites this paper as [16]
In the citing paper
“It showed that the results are in favor of better efficacy findings in industry‐funded studies than other sources of funding (risk ratio [RR]: 1.27; 95% confidence interval [CI]: 1.17–1.37).”
From The Cochrane risk of bias assessment tool 2 (RoB 2) versus the original RoB: A perspective on the pros and cons (2024) · cites this paper as [16]
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Metadata
- 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)
- Herlev Hospital (DK)
- Venue
- Cochrane Database of Systematic Reviews
- Publisher
- Wiley
- Published
- 2017-02-16
- Volume
- 2017
- Issue
- 2
- Pages
- MR000033-MR000033
- ISSN
- 1361-6137 (ISSN-L)
- 1465-1858
- PMC
- PMC8132492
- OpenAlex
- W1714980748
- Semantic Scholar
- 22ef5f94e3a061ee7d5d9a1a495601cbc87f08bd
- Cited by
- 1,697
- References
- 302
- Citations by year
- 2026: 71 · 2025: 113 · 2024: 98 · 2023: 127 · 2022: 135 · 2021: 131 · 2020: 159 · 2019: 143 · 2018: 152 · 2017: 146
- FWCI
- 28.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cochrane Database of Systematic Reviews (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, other-oa)
- Funders
- Université Paris Diderot
- National Institute of Mental Health
- Topics
- Health Sciences › Nursing › Issues, ethics and legal aspects › Pharmaceutical industry and healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Global Public Health Policies and Epidemiology
- Social Sciences › Business, Management and Accounting › Strategy and Management › Political Influence and Corporate Strategies
- Keywords
- pharmaceutical industry sponsorship, conflicts of interest, risk of bias, pharmaceutical research, research funding sources, publication bias, selective reporting, peer review, editorial policy, editorial independence
- MeSH terms
- Data Interpretation, Statistical, Drug Industry, Equipment and Supplies, Industry, Research Support as Topic, Conflict of Interest, Treatment Outcome, Publication Bias, Research Report
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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