June 3, 2015

Cancer correlation and causation

It’s a change to have a nice simple correlation vs causation problem. The Herald (from the Telegraph) says

Statins could cut the risk of dying from cancer by up to half, large-scale research suggests. A series of studies of almost 150,000 people found that those taking the cheap cholesterol-lowering drugs were far more likely to survive the disease.

Looking at the conference abstracts,  a big study found a hazard ratio of 0.78 based on about 3000 cancer deaths in women and a smaller study found a hazard ratio of 0.57 based on about half that many prostate cancer deaths (in men, obviously). That does sound impressive, but it is just a correlation. The men in the prostate cancer studies who happened to be taking statins were less likely to die of cancer; the women in the Women’s Health Initiative studies who happened to be taking statins were less likely to die of cancer.

There’s a definite irony that the results come from the Women’s Health Initiative. The WHI, one of the most expensive trials ever conducted, was set up to find out if hormone supplementation in post-menopausal women reduced the risk of serious chronic disease. Observational studies, comparing women who happened to be taking hormones with those who happened not to be, had found strong associations. In one landmark paper, women taking estrogen had almost half the rate of heart attack as those not taking estrogen, and a 22% lower rate of death from cardiovascular causes. As you probably remember, the WHI randomised trials showed no protective effect — in fact, a small increase in risk.

It’s encouraging that the WHI data show the same lack of association with getting cancer that summaries of randomised trials have shown, and that there’s enough data the association is unlikely to be a chance finding. As with estrogen and heart attack there are biochemical reasons why statins could increase survival in cancer. It could be true, but this isn’t convincing evidence.

Maybe someone should do a randomised trial.

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Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »

Comments

  • avatar
    Martin Kealey

    Whenever I hear “X cuts the risk of dying from Y”, I am minded to think “yes, and so does base-jumping without a parachute” (provided Y isn’t base-jumping, of course).

    2 years ago

    • avatar
      Thomas Lumley

      That particular issue isn’t actually a problem here, because the analysis is of rates rather than risks. An epidemic of parachute-free base-jumping would decrease the effective sample size for assessing the rates, but it wouldn’t systematically bias the rate downwards.

      It’s unfortunate that everyone says ‘risk’ even when they mean ‘rate’. Even experts do it, we just pretend it’s to simplify terminology.

      2 years ago