The 4-Hour Body_ An Uncommon Guide to Ra - Timothy Ferriss [206]
If your results are completely negative, don’t publish them at all, or publish them only after a long delay. This is exactly what the drug companies did with the data on SSRI antidepressants: they hid the data suggesting they might be dangerous, and they buried the data showing them to perform no better than placebo. If you’re really clever and have money to burn, then after you get disappointing data, you could do some more trials with the same protocol in the hope that they will be positive. Then try to bundle all the data up together, so that your negative data is swallowed up by some mediocre positive results.
Or you could get really serious and start to manipulate the statistics. For two pages only, this will now get quite nerdy. Here are the classic tricks to play in your statistical analysis to make sure your trial has a positive result.
Ignore the protocol entirely
Always assume that any correlation proves causation. Throw all your data into a spreadsheet programme and report—as significant—any relationship between anything and everything if it helps your case. If you measure enough, some things are bound to be positive just by sheer luck.
Play with the baseline
Sometimes, when you start a trial, quite by chance the treatment group is already doing better than the placebo group. If so, then leave it like that. If, on the other hand, the placebo group is already doing better than the treatment group at the start, then adjust for the baseline in your analysis.
Ignore dropouts
People who drop out of trials are statistically much more likely to have done badly, and much more likely to have had side- effects. They will only make your drug look bad. So ignore them, make no attempt to chase them up, do not include them in your final analysis.
Clean up the data
Look at your graphs. There will be some anomalous ‘outliers’, or points which lie a long way from the others. If they are making your drug look bad, just delete them. But if they are helping your drug look good, even if they seem to be spurious results, leave them in.
‘The best of five … no … seven … no … nine!’
If the difference between your drug and placebo becomes significant four and a half months into a six-month trial, stop the trial immediately and start writing up the results: things might get less impressive if you carry on. Alternatively, if at six months the results are ‘nearly significant’, extend the trial by another three months.
Torture the data
If your results are bad, ask the computer to go back and see if any particular subgroups behaved differently. You might find that your drug works very well in Chinese women aged fifty-two to sixty-one. ‘Torture the data and it will confess to anything’, as they say at Guantanamo Bay.
Try every button on the computer
If you’re really desperate, and analysing your data the way you planned does not give you the result you wanted, just run the figures through a wide selection of other statistical tests, even if they are entirely inappropriate, at random.
And when you’re finished, the most important thing, of course, is to publish wisely. If you have a good trial, publish it in the biggest journal you can possibly manage. If you have a positive trial, but it was a completely unfair test, which will be obvious to everyone, then put it in an obscure journal (published, written and edited entirely by the industry). Remember, the tricks we have just described hide nothing, and will be obvious to anyone who reads your paper, but only if they read it very attentively, so it’s in your interest to make sure it isn’t read beyond the abstract. Finally, if your finding is really embarrassing,