I wholeheartedly believe in clinical trials to further progress in
medical practice--I abhor the manipulation of statistics, data
reporting or lack of, etc.  the following is a guide in how to cheat in
clinical trials with examples from current studies:

There's an excellent article in this month's Internal Medicine journal
by Dr Ian Scott, of the Princess Alexandra Hospital in Brisbane. He
describes the top 15 tricks used to skew the findings or interpretation
of clinical trials. He cites them as examples to watch out for, but his
list could also be used as a cheat's manual for any drug company
clinical trial designer. the following is the listing:
1. Generalise your findings from an unrepresentative group.

Example: The RALES trial showed spironolactone helped in heart failure
- but practice showed that this wasn't the case for anyone with renal
failure or mild LV dysfucntion [who were not included in the trial].

2. Find a dodgy comparator.

Example: Compare high dose Lipitor with less potent doses of
Pravastatin, as in the recent TNT study.

3. Use a surrogate end point, not a clinically important one.

Example: If you have an expensive anti-Alzheimer's drug, show it makes
some differences to cognitive function and then claim this will result
in less need for institutionalisation, reduced disability, fewer deaths
or adverse events, lower carer burden and decreased health-care costs.
A recent trial of donepezil shows it doesn't.

4. Always emphasise the relative rather than absolute benefits.

Example: Treating patients with moderate to severe hypertension will
prevent more strokes (ARR = 8%; NNT = 12) than treating mild
hypertension (ARR = 0.6%; NNT = 166), even though the relative risk
reduction for antihypertensives is identical (40%) for both groups.

5. Emphasise statistical significance and play down effect size.

Example: An Australian trial in 6000 patients found that ACE inhibitors
were beter than diuretics in elderly hypertensive patients. The much
more powerful ALLHAT trial didn't.

6. Dig deep - there's always good news in subgroup analyses.

Example: Pfizer's Praise trial of amlodipine found a highly significant
survival benefit in a non-ischaemic paient subgroup. Not seen in
subsequent studies.

7. De-emphasise harmful effects - or even better, don't measure them at
all.

Example: Vioxx and cardiovascular risk - why did it take four years to
show this? So much for post marketing surveillance.

8. Composite end points can show anything if you try.

Example: The UKPDS trial of intensive glycemic control found a
significant benefit on "first diabetes-related events" but this was
made up of 21 end points. Most of this effect comprised reduction in
retinal photocoagulation, with no changes in diabetes-related deaths
and all-cause mortality.

9. Clinician-initiated end points can mean anything.

Example: Endpoints like revascularisation procedures and initiation of
dialysis are arbitrary, proxy endpoints that may vary with the
environment and may not reflect the natural history of the disease.

10. Secondary endpoints may save the day.

Example: The ELITE I trial of elderly patients with heart failure using
either losartan or captopril found no difference in renal function as
the primary end-point. An unexpected decrease was seen in the secondary
end-point of all-cause mortality favouring losartan, not confirmed by
subsequent trials.

11. Conflated trials: aggregate the data, confuse the punters.

Example: the PROGRESS study was in effect two trials, with patients in
one arm randomised, according to clinician preference, to perindopril
plus indapamide or perindopril alone. The separate results for each
trial showed perindopril alone had no outcome effect, a result
de-emphasised in several interpretations of PROGRESS results
recommending perindopril be initiated post-stroke.

12. It's a class effect!

Example: Class effcts of ACE inhibitors in patients with stable
cardiovascular disease and preserved left ventricular function? Not
according to mixed results from HOPE, EUROPA, and PEACE studies.

13. Do an equivalence trial with fuzzy margins

Example: The INJECT trial of thrombolytics.

14. Sponsored trials have sunny summaries.

Example: "The inconsistencies in data analysis and reporting suggests
to us a biased attempt to present ESSENCE in a positive light. Four of
7 authors and 4 of 7 members of the trial executive committee were, or
had previously been, drug company employees; the trial executive
chairman and the lead author both received company research grants; and
the company's research and development centre undertook data
co-ordination."

15. Negative trials never see daylight.

Examples: Glaxosmithkline's latest Serevent data on paradoxical
bronchoconstriction. A prospective follow up of 126 trials submitted to
the ethics committee of a major Sydney tertiary hospital, those with
significantly positive results were more likely to be published (85 vs
65% over 10 years), and be published earlier (median time to
publication 4.8 years vs 8.0 years) than trials showing nil effect.