Abstract
Industry funding of research is the greatest known systematic threat to the objectivity of medical research. This paper clarifies the nature and scope of industry funding bias and attempts to quantify it. It reviews four kinds of remedy for industry bias suggested so far: disclosure, standards and regulation, steps towards independence for all clinical research, and case by case assessments, finding most of them helpful but not sufficient. The paper proposes two possible further interventions to reduce the effects of industry bias: qualitative and quantitative discounting of industry results.
My thanks to John R. Clarke for discussions about quantitative discounting. I also thank Adam LaCaze and Barbara Osimani for their helpful comments on an earlier draft of this paper.
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- 1.
This means that there will be more biases to correct for even when (or if) industry bias is taken care of. For example, publication bias and time-to-publication bias reduce and slow the publication of negative results even in independent research.
- 2.
I am grateful to Adam LaCaze for suggesting this.
- 3.
This use of the term “discount” should not be confused with a economics/decision science of the term to describe discounting future utilities (temporal discounting).
- 4.
In the GRADE system of rating, a high quality observational trial can count as highest quality of evidence. But most ranking systems allow only RCTs as best evidence.
- 5.
In this study, the physicans were practicing obstetrician-gynecologists. It is possible that more research oriented readers would discount.
- 6.
These perverse effects were mostly found in disclosures between physicians and patients. It is not known how much they affect the authors and readers of medical research literature.
- 7.
See http://www.icmje.org/icmje-recommendations.pdf, accessed September 5, 2016.
- 8.
See http://www.consort-statement.org/consort-2010, accessed September 5, 2016
- 9.
Adam LaCaze has brought the website http://compare-trials.org to my attention. It is run by a team of researchers at the Center for Evidence-Based Medicine, Oxford University, headed by Ben Goldacre. It tracks unreported and switched outcomes. Hopefully, this will reduce selective reporting.
- 10.
Informal communication.
- 11.
The recent GRADE system is an exception that includes high quality observational trials in level 1.
- 12.
My thanks to John R. Clarke for walking me through this calculation of quantitative discounting.
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Solomon, M. (2020). After Disclosure. In: LaCaze, A., Osimani, B. (eds) Uncertainty in Pharmacology. Boston Studies in the Philosophy and History of Science, vol 338. Springer, Cham. https://doi.org/10.1007/978-3-030-29179-2_19
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