Read Bad Pharma: How Drug Companies Mislead Doctors and Harm Patients Online
Authors: Ben Goldacre
Bad Pharma: How Drug Companies Mislead Doctors and Harm Patients (56 page)
127
http://www.propublica.org/article/piercing-the-veil-more-drug-companies-reveal-payments-to-doctors
128
Carlowe J. Drug companies to declare all payments made to doctors from 2012. BMJ. 2010 Nov 5;341(nov05 1):c6290–c6290.
129
Tuffs A. Two doctors in Germany are convicted of taking bribes from drug company. BMJ. 2010 Nov 9;341(nov09 2):c6359–c6359.
130
http://www.fcaalert.com/2011/02/articles/dojhhs-releases-new-statistics-about-sealed-qui-tam-cases/
131
Sweet M. Experts criticise industry sponsorship of articles on health policy in Australian newspaper. BMJ. 2011 Oct 25;343(oct25 2):d6903–d6903.
132
http://www.pmcpa.org.uk/?q=node/499
133
http://www.propublica.org/documents/item/87376-heart-rhythm-society
134
http://www.propublica.org/article/medical-groups-shy-about-detailing-industry-financial-support
135
JP Kassirer. On the Take: How Medicine’s Complicity with Big Business Can Endanger Your Health. 1st ed. Oxford University Press, USA; 2004.
136
http://www.eatright.org/corporatesponsors/.
137
JP Kassirer. On the Take: How Medicine’s Complicity with Big Business Can Endanger Your Health. 1st ed. Oxford University Press, USA; 2004, p105.
138
Choudhry NK, Stelfox HT, Detsky AS. Relationships between authors of clinical practice guidelines and the pharmaceutical industry. JAMA. 2002 Feb 6;287 (5):612–7.
Afterword: Better Data
1
Department of Justice, Office of Public Affairs. GlaxoSmithKline to Plead Guilty and Pay $3 Billion to Resolve Fraud Allegations and Failure to Report Safety Data. Monday, July 2, 2012. http://www.justice.gov/opa/pr/2012/July/12-civ-842.html
2
Glaxo executives cited in case now lead Sanofi, Actelion. Bloomberg News, 3/7/12. http://www.businessweek.com/news/ 2012-07-03/glaxo-executives-cited-in-case-now-lead-sanofiactelion
3
Inpharm 4/7/12. GSK ruling: another failing, but will the industry learn? http://www.inpharm.com/news/173307/gsk-ruling-another-failing-will-industry-learn
4
Glaxo Agrees to Pay $3 Billion in Fraud Settlement. New York Times, July 2 2012. http://www.nytimes.com/2012/07/03/ business/glaxosmithkline-agrees-to-pay-3-billion-in-fraud-settlement.html
5
Level playing field push to continue despite setback – 8 December 2011. Medicines Australia. http://medicinesaustralia. com.au/2011/12/08/level-playing-field-push-to-continue-despite-setback/
6
Drug companies to work with CCGs on care pathways and case finding under DH-backed scheme. Pulse, 28 May 2012, http://www.pulsetoday.co.uk/ newsarticle-content/-/article_ display_list/14029608/drug-companies-to-work-with-ccgs-on-care-pathways-and-case-finding-under-dh-backed-scheme.
7
Bosch X, Esfandiari B, McHenry L. Challenging Medical Ghostwriting in US Courts. PLoS Med. 2012 Jan 24;9(1):e1001163.
Illustrations
Fig. 1
: http://www.cochrane.org/about-us/history/our-logo%23files
Fig. 2
: Mulrow CD. Rationale for systematic reviews. BMJ. 1994 Sep 3;309(6954):597–9. Available at: http://www.ncbi.nlm. nih.gov/pmc/articles/PMC2541393/?page=1;
Fig. 3
: Ranibizumab and pegaptanib for the treatment of age-related macular degeneration: a systematic review and economic evaluation. NICE 2006. Available at: http://www.nice.org.uk/ nicemedia/live/11700/34991/34991.pdf
Fig. 4
: http://www.prescrire.org/editoriaux/EDI33693.pdf
Fig. 6
: Carpenter D. Reputation and Power: Organizational Image and Pharmaceutical Regulation at the FDA. Princeton University Press, 2010.
Fig. 7
: Schwartz LM, Woloshin S, Welch HG. Using a Drug Facts Box to Communicate Drug Benefits and Harms Two Randomized Trials. Ann Intern Med. 2009 Apr 21;150(8):516–27. Available at: http://dartmed.dartmouth.edu/spring08/pdf/ disc_drugs_we/lunesta_box.pdf
Fig. 8
: http://www.lunesta.com/PostedApprovedLabelingText.pdf
Fig. 9
: Lurie P, Wolfe SM. Misleading data analyses in salmeterol (SMART) study. The Lancet. 2005 Oct;366(9493): 1261–2.
Fig. 11
: Rothwell PM. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation. The Lancet. 2005;365(9454):176–86. Available at: http://apps.who.int/ rhl/Lancet_365-9454.pdf
Fig. 12
: Moynihan R. The making of a disease: female sexual dysfunction. BMJ. 2003;326(7379):45–47. Available at: http:// www.ncbi.nlm.nih.gov/pmc/articles/PMC1124933/table/TN0x95f 06b0.0x98eca30/
Fig. 13
, fig. 14 (p.294 bottom), fig. 15 (p.295 top), fig. 16 (p.295 bottom): http://zyprexalitigationdocuments.com/%5 Cdocuments%5CConfidentiality-Challenge%5CDocs-challenged-in-10-3-list%5C145-ZY200187608-7614.pdf
Fig. 17
: Drug Industry Document Archive [Internet]. Available at: http://dida.library.ucsf.edu/pdf/vou38h10
Fig. 18
: http://pogoblog.typepad.com/pogo/gw-attachment-e.html
Fig. 19
: 15 August 2012, http://uk.finance.yahoo.com/ echarts?s=GSK.L#symbol=GSK.L;range=1y
BEN GOLDACRE
is a doctor and writer. His first book Bad Science reached Number One in the nonfiction charts, sold over 400,000 copies in the UK alone, and has been translated into 25 languages. He is 38 and lives in London.
First published in Great Britain in 2012 by
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Copyright © Ben Goldacre 2012
1
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*
Instead of designing elaborate new studies to see whether people could consciously see forward in time, Bem simply ran some classic psychology experiments backwards. So, for example, he conducted a well-known experiment on subliminal influence, where you show people two mirror images of the same picture, and then ask them which they prefer; but you flash up an unpleasant subliminal image underneath one or other image for just a few milliseconds before they make their choice. In the normal run of this study, the subliminal image makes people less likely to choose that option. In the Bem study, the unpleasant subliminal images were flashed up just
after
the participants made their choice of favourite image. However unlikely it sounds, Bem found that these subliminal images still had an effect on people’s choices.
*
Iain Chalmers was knighted for setting up the Cochrane Collaboration. Being very practical people, the researchers at Cochrane wanted to know if there was any real value in this, so they ran a randomised trial. Were recipients of letters from ‘Iain Chalmers’ more or less likely to respond, they wondered, if he signed his name ‘Sir Iain Chalmers’? A simple system was set up, and just before they were posted, outgoing letters were randomly signed ‘Sir Iain Chalmers’ or just ‘Iain Chalmers’. The researchers then compared the number of replies to each signature: and the ‘Sir’ made no difference at all. This study is published in full – despite reporting a negative result – in the
Journal of the Royal Society of Medicine
, and it is not a flippant subject for research. There are many knights in medicine; there are troubling things you can do to increase your chance of becoming one; and lots of people think, ‘If I was a knight, people would take my good ideas much more seriously.’ The paper is titled ‘Yes Sir, No Sir, not much difference Sir’.
54
After reading it, you can relax your ambitions.
*
Setting out a simple list of trials is also important for other reasons, including something called ‘duplicate publication’. A British anaesthetist called Martin Tramèr conducted a review on the efficacy of a nausea drug called ondansetron, and noticed that lots of the data seemed to be replicated. On closer inspection, it turned out that lots of trials had been conducted in lots of different places, and then bundled up into multi-centre trials.
77
But the results for many individual patients had been written up several times over, bundled up with other data, in different journals and different papers. Crucially, data which showed the drug in a better light were more likely to be duplicated than the data which showed it to be less impressive; overall this led to a 23 per cent overestimate of the drug’s efficacy.
*
If this is of particular interest to you, it’s covered at length in my previous book,
Bad Science
.
*
This is tough, but here’s how to think through the derivation of the 3/
n
rule, if you’re statistically inclined. Let’s say we’re eating week-old chicken, and the probability of death is 0.2, so the probability of no death is (1–0.2), which is 0.8. If we have two observations – I eat mouldy chicken twice – then the probability of ‘no death’ is less: it’s 0.8 x 0.8, or 0.64 (so, my chances of death are rising with every meal of mouldy chicken I eat). If I eat mouldy chicken
n
times, the probability of no death is 0.8^
n
, or, to go back to where the 0.8 came from, that’s (1–0.2)^
n
, or more generally (1–risk)^
n
. Now we want to sit at the other end of the telescope. We want to know the
maximum
possible risk of something happening that is compatible with never having seen it happen, after
n
observations (or mouldy-chicken meals), with at most a 5 per cent margin of error. In equation terms, we would say that (1–risk)^
n
equals 0.05, or rather, since we’re not interested in (1–risk), but in (1–maximum risk), we’d say (1–maximum risk)^
n
=0.05. Now we just have to rearrange that equation, to make it give us maximum risk when we know
n
. The calculus-wrestling goes: 1–maximum risk = 0.05^(1/
n
), and for
n
greater than 30 that’s approximately the same as 1–maximum risk = 1–(3/
n
). We’re nearly there: take away the ‘one minus’ on both sides and you have maximum risk = 3/
n
. That may have been a bit tougher than your average Vorderman maths session, but it is much more useful. ‘I’ve Never Met a Nice South African’ is a racist song about racists. Now you know to ask: ‘How many have you met?’
