Aspirin or bias?

Update 4PM Eastern, 3/21/12

Wanted to append a couple of thoughts I tweeted earlier about these studies, in case you don't follow me on Twitter or just missed them:

Around 2:30 PM Eastern:

And then closer to 3:00 PM Eastern: 

Thoughts?


There is a fascinating review by Cancer Research UK of the new and old aspirin data with respect to its effects on cancer and cardiovascular complications in the context of a heightened risk for bleeding. The review is full of fabulous information about what we know and the uncertainties that remain, all with practical suggestions at the end, so go there and read it.

But here is what I wanted to highlight in the graph that I am reproducing from a screen shot:

There is something very interesting going on here. Just as the risk of bleeding begins to drop, so does the risk of developing cancer. This could be a complete coincidence, but perhaps not. An alternative explanation is that people who already have cancer, though it may not yet be diagnosed, may be at a higher risk for a bleeding complication. Those who develop a bleeding complication presumably are taken off aspirin. But remember, they may already be harboring a cancer that will rear its head in the near future. But what about those who do not bleed and therefore are able to tolerate aspirin for a longer time? They also seem to have a drop in their risk of incident cancer. But of course this may have nothing to do with aspirin's preventing cancer, so much as with its ability to unmask a cancer that is already present and essentially weed them out from the future risk pool for cancer development. And when you weed out those at a higher risk for clinical cancer, by definition you have a group with a lower than standard risk, creating the potential for a selection bias. Make sense?

Conversely, the risk of a cardiac event starts to increase roughly at the same time as the risks for cancer and bleeding begin to drop. This to me suggests confirmation that aspirin may prevent cardiovascular events early in the course of taking it. Furthermore, given my hypothesis above about aspirin's weeding out those with an early cancer, perhaps its cardiovascular impact is for some reason limited to those with an early cancer or with another reason for aspirin-induced bleeding.

All-in-all the data do not convince me to start taking aspirin -- I am still at odds with Dr. Agus on that. The selection bias that I described above may very well mean that aspirin's role is not as a cancer prevention, but more likely as a sort of a stress test for those with a subclinical cancer. So we are left again with the the chicken-and-egg question. But isn't that, after all, what makes science exciting?

Would love to know what others think -- does this make sense? Are there other possible explanations?      

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Some thoughts on denominators

I decided to re-post this piece dating back from May 2009 in the wake of the recent reports about bird flu. As it turns out, this flu may not be quite as deadly as we had previously thought. And, yes, this revelation is all about the denominator.

Let's face it: denominators keep numbers (and people reporting them) honest. Imagine if I said that there were 3,352 cases of a never-before-seen strain of flu in the US. To be sure, 3,352 cases is a large enough number to send us rushing to buy a respirator mask! But what if I put it slightly differently and said that out of the population of roughly 300,000,000 individuals, 3,352 have contracted this strain of flu. I think this makes things a little different, since it means that the risk of contracting this flu to date is about 1 in 100,000, a fairly low number as risks go. Now, I am going to give you another number -- 86. This represents the number of the novel H1N1 flu-related deaths in Mexico reported on April 25, 2009, by the health minister of Mexico, and at that time this flu had been thought to have sickened 1,400 people. This gives us the risk of death with the flu of roughly 6%, a very high risk indeed! Well, that was then. Now that we have all steadied our pulses, and the health authorities have gone back and done some testing, as of yesterday Mexico had confirmed 2,059, cases with 56 fatalities, equating to a 2.7% risk of dying with the disease. Still a high number, to be sure, but lower than what was though before.

In the US, we have had 3 fatalities among 3,352 cases reported as of yesterday, yielding the risk of death from H1N1 in this country of about 1 in 1,000. But, of course, the denominator of 3,352 persons represent only those who sought medical attention and got tested, so probably it is an underestimate of the true burden of this strain of flu, and necessarily also an over-estimate of its attendant mortality. Now, apply this to the situation in Mexico, and it's likely that the risk of death from H1N1 is also lower than what we have observed precisely due to the under-estimation of the denominator. 

So how could we get a true estimate of the numbers of people afflicted with the H1N1 influenza? Well, we could screen absolutely everyone (or more likely a large and representative group of individuals). Then what? Do we treat them all with anti-virals? Do we observe them? Since the Centers for Disease Control and Prevention recommends testing only severe cases and treating only persons at a high risk for complications, universal testing does not seem like a practical approach. So, the bottom line is that we are not likely ever to get at the correct denominator for the risk of dying with this disease, and any number that we get is likely to be an over-estimate of the true risk.

So, what are the lessons here? First, don't let anyone get away with only giving you the numerator, as that is not even a half of the story. Second, even when the denominator appears known, be skeptical -- does it really represent the entire pool of cases that are at risk for the event that the numerator describes? The likely answer will most of the time be "no". Clearly, it is the denominator that is the key to being an educated consumer of health information.