Bad Pharma: How Drug Companies Mislead Doctors and Harm Patients Read Online Free PDF Page A

baby. You’ll need a reasonable number of them, let’s say two hundred for this trial. Then you divide them into two groups at random, give the mothers in one group the current best treatment (whatever that is in your town), while the mothers in the other group get current best treatment plus some steroids. Finally, when all two hundred women have gone through your trial, you count up how many babies survived in each group.
    This is a real-world question, and lots of trials were done on this topic, starting from 1972 onwards: two trials showed that steroids saved lives, but five showed no significant benefit. Now, you will often hear that doctors disagree when the evidence is mixed, and this is exactly that kind of situation. A doctor with a strong pre-existing belief that steroids work – perhaps preoccupied with some theoretical molecular mechanism, by which the drug might do something useful in the body – could come along and say: ‘Look at these two positive trials! Of course we must give steroids!’ A doctor with a strong prior intuition that steroids were rubbish might point at the five negative trials and say: ‘Overall the evidence shows no benefit. Why take a risk?’
    Up until very recently, this was basically how medicine progressed. People would write long, languorous review articles – essays surveying the literature – in which they would cite the trial data they’d come across in a completely unsystematic fashion, often reflecting their own prejudices and values. Then, in the 1980s, people began to do something called a ‘systematic review’. This is a clear, systematic survey of the literature, with the intention of getting all the trial data you can possibly find on one topic, without being biased towards any particular set of findings. In a systematic review, you describe exactly how you looked for data: which databases you searched, which search engines and indexes you used, even what words you searched for. You pre-specify the kinds of studies that can be included in your review, and then you present everything you’ve found, including the papers you rejected, with an explanation of why. By doing this, you ensure that your methods are fully transparent, replicable and open to criticism, providing the reader with a clear and complete picture of the evidence. It may sound like a simple idea, but systematic reviews are extremely rare outside clinical medicine, and are quietly one of the most important and transgressive ideas of the past forty years.
    When you’ve got all the trial data in one place, you can conduct something called a meta-analysis, where you bring all the results together in one giant spreadsheet, pool all the data and get one single, summary figure, the most accurate summary of all the data on one clinical question. The output of this is called a ‘blobbogram’, and you can see one on the opposite page, in the logo of the Cochrane Collaboration, a global, non-profit academic organisation that has been producing gold-standard reviews of evidence on important questions in medicine since the 1980s.
    This blobbogram shows the results of all the trials done on giving steroids to help premature babies survive. Each horizontal line is a trial: if that line is further to the left, then the trial showed steroids were beneficial and saved lives. The central, vertical line is the ‘line of no effect’: and if the horizontal line of the trial touches the line of no effect, then that trial showed no statistically significant benefit. Some trials are represented by longer horizontal lines: these were smaller trials, with fewer participants, which means they are prone to more error, so the estimate of the benefit has more uncertainty, and therefore the horizontal line is longer. Finally, the diamond at the bottom shows the ‘summary effect’: this is the overall benefit of the intervention, pooling together the results of all the individual trials. These are much narrower than the lines