Data dredging, bias, or confounding

Abstract

On 4 October 2002, women who were moderate drinkers received good news: their risk of breast cancer was not raised, according to a report in the Lancet that was widely covered by the British media.1 The bad news was that smoking at an early age was now implicated as a risk factor for breast cancer. However, after they had enjoyed guilt-free drinks (without cigarettes) for only a few days, on 13 November the message was reversed: alcohol did increase the risk of breast cancer after all, but smoking was declared innocent.2 The press release proclaimed “Alcohol, tobacco and breast cancer: the definitive answer.” A reader was driven to complain in the letters page of the Guardian (14 November 2002): “So let me get this right—alcohol's no good anymore, and if you smoked within five years of getting your periods, that's bad news too. Oh no, that was a couple of weeks ago; smoking's okay now … Do things stop being bad for us if we just forget about them for a bit, do you think?” This is a familiar story—so much so that in Bristol we set our medical students the exercise of examining the “health scare of the week” that appears each Friday, generally from a study reported in the BMJ or Lancet .w1 The widespread perception that epidemiological studies generate conflicting and often meaningless findingsw2 has received support from recent randomised controlled trials, which have failed to confirm even apparently robust findings from observational epidemiological studies. The most topical of these relates to hormone replacement therapy. In 1991 a meta-analysis of epidemiological results relating the use of hormone replacement therapy to the risk of coronary heart disease concluded that it halved the risk, and that the evidence was statistically robust (relative risk 0.50; 95% …

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Affiliated Institutions

• University of Bristol GB

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