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Associations between industry involvement and study characteristics at the time of trial registration in biomedical research


Autoři: Anna Lene Seidler aff001;  Kylie E. Hunter aff001;  Nicholas Chartres aff002;  Lisa M. Askie aff001
Působiště autorů: NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia aff001;  The University of Sydney, Sydney, Australia aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222117

Souhrn

Background

Commercial or industry funding is associated with outcomes that favour the study funder in published studies, across various areas of research. However, it is currently unclear whether there are differences between trials with and without industry involvement at the stage of trial registration.

Objective

To determine whether industry involvement (industry sponsorship, funding, or collaboration) is associated with trial characteristics at the time of trial registration.

Methods

We conducted a cross-sectional analysis of all interventional studies registered on the Australian New Zealand Clinical Trials Registry in 2017 and classified them by industry involvement. We analysed whether there were differences in study characteristics (including type of control, sample size, study phase, randomisation, registration timing, and purpose of study) by industry involvement.

Results

Industry involvement was reported by 21% of the 1,433 included trials. Only 40% of trials with industry involvement used an active control compared to 58% of non-industry trials (OR = 0.49, 95%CI = 0.38 to 0.63, p < .001), and industry trials reported smaller sample sizes (Median(IQR)industry = 45(24–100), Median(IQR)non-industry = 70(35–160), Mean Difference = -153, 95% CI = -233 to -75, p < .001). Industry trials were more likely to be earlier phase trials (Χ2(df) = 71.46(4), p < .001). There was no difference in use of randomisation between industry (70%) and non-industry trials (73%) (OR = 0.88, 95%CI = 0.67–1.20, p = .38). Eighty-three percent of industry trials compared to 70% of non-industry trials were prospectively registered (OR = 2.02, 95%CI = 1.47–2.82, p < .001). Industry trials were more likely to assess treatment (85%), rather than prevention, education or diagnosis compared to non-industry trials (64%) (OR = 3.02, 95%CI = 2.17–4.32, p < .001).

Conclusion

The current study gives insight into differences in trial characteristics by industry involvement at registration stage. There was a reduced use of active controls in trials with industry involvement which has previously been proposed as a mechanism behind more favourable results. Non-industry funders and sponsors are crucial to ensure research addresses not only treatments, but also prevention, diagnosis and education questions.

Klíčová slova:

Clinical trials – Drug research and development – Finance – Government funding of science – Industrial organization – Preventive medicine – Research funding – Research grants


Zdroje

1. Bodenheimer T. Uneasy Alliance—Clinical Investigators and the Pharmaceutical Industry. New England Journal of Medicine. 2000;342(20):1539–44. doi: 10.1056/NEJM200005183422024 10816196.

2. Bero LA. Why the Cochrane risk of bias tool should include funding source as a standard item. The Cochrane database of systematic reviews. 2013;(12):Ed000075. Epub 2014/02/28. doi: 10.1002/14651858.ED000075 24575439.

3. Sterne JA. Why the Cochrane risk of bias tool should not include funding source as a standard item. The Cochrane database of systematic reviews. 2013;(12):Ed000076. Epub 2014/02/28. doi: 10.1002/14651858.ED000076 24575440.

4. Rosenbaum L. Conflicts of interest: part 1: Reconnecting the dots—reinterpreting industry-physician relations. The New England journal of medicine. 2015;372(19):1860–4. Epub 2015/05/07. doi: 10.1056/NEJMms1502493 25946288.

5. Every-Palmer S, Howick J. How evidence-based medicine is failing due to biased trials and selective publication. Journal of evaluation in clinical practice. 2014;20(6):908–14. Epub 2014/05/14. doi: 10.1111/jep.12147 24819404.

6. White J BL. Corporate manipulation of research: Strategies are similar across five industries. Stanford Law & Policy Review. 2010;21(1):105–34.

7. Hart B, Lundh A, Bero L. Effect of reporting bias on meta-analyses of drug trials: reanalysis of meta-analyses. BMJ (Clinical research ed). 2012;344:d7202. doi: 10.1136/bmj.d7202 22214754

8. Bero L, Oostvogel F, Bacchetti P, Lee K. Factors associated with findings of published trials of drug-drug comparisons: why some statins appear more efficacious than others. PLoS Medicine / Public Library of Science. 2007;4(6):e184. doi: 10.1371/journal.pmed.0040184 17550302; PubMed Central PMCID: PMC1885451.

9. Yank V, Rennie D, Bero LA. Financial ties and concordance between results and conclusions in meta-analyses: retrospective cohort study. BMJ (Clinical research ed). 335(7631):1202–5. doi: 10.1136/bmj.39376.447211.BE 18024482.

10. Bero L, Anglemyer A, Vesterinen H, Krauth D. The relationship between study sponsorship, risks of bias, and research outcomes in atrazine exposure studies conducted in non-human animals: Systematic review and meta-analysis. Environment international. 2015. Epub 2015/12/24. doi: 10.1016/j.envint.2015.10.011 26694022.

11. Barnes DE, Bero LA. Industry-funded research and conflict of interest: an analysis of research sponsored by the tobacco industry through the Center for Indoor Air Research. Journal of Health Politics, Policy & Law. 1996;21(3):515–42. 8784687.

12. Barnes DE, Bero LA. Scientific quality of original research articles on environmental tobacco smoke. Tobacco Control. 1997;6(1):19–26. doi: 10.1136/tc.6.1.19 9176982; PubMed Central PMCID: PMC1759539.

13. Barnes DE, Bero LA. Why review articles on the health effects of passive smoking reach different conclusions. Jama. 1998;279(19):1566–70. doi: 10.1001/jama.279.19.1566 9605902.

14. Cho MK, Bero LA. The quality of drug studies published in symposium proceedings. Ann Intern Med. 1996;124(5):485–9. Epub 1996/03/01. doi: 10.7326/0003-4819-124-5-199603010-00004 8602706.

15. Lundh A, Lexchin J, Mintzes B, Schroll JB, Bero L. Industry sponsorship and research outcome. Cochrane Database of Systematic Reviews. 2017;(2). doi: 10.1002/14651858.CD012545 PubMed PMID: MR000033.

16. Bero L, Oostvogel F, Bacchetti P, Lee K. Factors associated with findings of published trials of drug–drug comparisons: why some statins appear more efficacious than others. PLoS Medicine. 2007;4(6):e184. doi: 10.1371/journal.pmed.0040184 17550302

17. Odierna DH, Forsyth SR, White J, Bero LA. The cycle of bias in health research: a framework and toolbox for critical appraisal training. Accountability in research. 2013;20(2):127–41. doi: 10.1080/08989621.2013.768931 23432773

18. Yank V, Rennie D, Bero LA. Financial ties and concordance between results and conclusions in meta-analyses: retrospective cohort study. BMJ. 2007;335(7631):1202–5. doi: 10.1136/bmj.39376.447211.BE 18024482

19. De Angelis C, Drazen JM, Frizelle FA, Haug C, Hoey J, Horton R, et al. Clinical Trial Registration: A Statement from the International Committee of Medical Journal Editors. New England Journal of Medicine. 2004;351(12):1250–1. doi: 10.1056/NEJMe048225 15356289.

20. World Medical Assocation. Declaration of Helsinki: Ethical principles for medical research involving human subjects. 2008.

21. World Health Organisation. International Clinical Trials Registry Platform (ICTRP). http://www.who.int/ictrp/trial_reg/en/index.html. (Accessed 23 Jan, 2019).

22. Bourgeois FT, Murthy S, Mandl KD. Outcome reporting among drug trials registered in ClinicalTrials. gov. Annals of internal medicine. 2010;153(3):158–66. doi: 10.7326/0003-4819-153-3-201008030-00006 20679560

23. Lundh A, Sismondo S, Lexchin J, Busuioc O, Bero L. Industry sponsorship and research outcome. The Cochrane database of systematic reviews. 2011;12.

24. Hunter KE, Seidler AL, Askie LM. Prospective registration trends, reasons for retrospective registration and mechanisms to increase prospective registration compliance: descriptive analysis and survey. BMJ Open. 2018;8(3):e019983. doi: 10.1136/bmjopen-2017-019983 29496896

25. R Core Team. R: A language and environment for statistical computing. 2013.

26. Califf RM, Zarin DA, Kramer JM, Sherman RE, Aberle LH, Tasneem A. Characteristics of clinical trials registered in clinicaltrials.gov, 2007–2010. JAMA. 2012;307(17):1838–47. doi: 10.1001/jama.2012.3424 22550198

27. Tan AC, Jiang I, Askie L, Hunter K, Simes RJ, Seidler AL. Prevalence of trial registration varies by study characteristics and risk of bias. Journal of clinical epidemiology. 2019. doi: 10.1016/j.jclinepi.2019.05.009 31121304


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2019 Číslo 9
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