The Therapeutic Paradox: What's Right for the Population May Not Be Right for the Patient

Bad for the population, good for me

An article in this week's New York Times called Will This Treatment Help Me?  There's a Statistic for that highlights the disconnect between the risks (and risk reductions) that epidemiologists, researchers, guideline writers, the pharmaceutical industry, and policy wonks think are significant and the risks (and risk reductions) patients intuitively think are significant enough to warrant treatment.

The authors, bloggers at The Incidental Economist, begin the article with a sobering look at the number needed to treat (NNT).  For the primary prevention of myocardial infarction (MI), if 2000 people with a 10% or higher risk of MI in the next 10 years take aspirin for 2 years, one MI will be prevented.  1999 people will have gotten no benefit from aspirin, and four will have an MI in spite of taking aspirin.  Aspirin, a very good drug on all accounts, is far from a panacea, and this from a man (me) who takes it in spite of falling far below the risk threshold at which it is recommended.

One problem with NNT is that for patients it is a gratuitous numerical transformation of a simple number that anybody could understand (the absolute risk reduction  - "your risk of stroke is reduced 3% by taking coumadin"), into a more abstract one (the NNT - "if we treat 33 people with coumadin, we prevent one stroke among them") that requires retransformation into examples that people can understand, as shown in pictograms in the NYT article.  A person trying to understand stroke prevention with coumadin could care less about the other 32 people his doctor is treating with coumadin, he is interested in himself.  And his risk is reduced 3%.  So why do we even use the NNT, why not just use ARR?

Absolute Confusion: How Researchers Mislead the Public with Relative Risk

This article in Sunday's New York Times about gauging the risk of autism highlights an important confusion in the appraisal of evidence from clinical trials and epidemiological studies that appears to be shared by laypersons, researchers, and practitioners alike:  we focus on relative risks when we should be concerned with absolute risks.

The rational decision maker, when evaluating a risk or a benefit, is concerned with the absolute magnitude of that risk or benefit.  A proportional change from an arbitrary baseline (a relative risk) is irrelevant.  Here's an example that should bring this into keen focus.

If you are shopping and you find a 50% off sale, that's a great sale.  Unless you're shopping for socks.  At $0.99 a pair, you save $0.50 with that massive discount.  Alternatively, if you come across a 3% sale, but it's at the Audi dealership, that paltry discount can save you $900 on a $30,000 Audi A4.   Which discount should you spend the day pursuing?  The discount rate mathematically obscures the value of the savings.  If we framed the problem in terms of absolute savings, we would be better consumers.  But retailers know that saying "50% OFF!" attracts more attention than "$0.50 OFF!" in the sock department.  Likewise, car salesmen know that writing "$1000 BELOW INVOICE!" on the windshield looks a lot more attractive than "3% BELOW INVOICE!"

Billions and Billions of People on Statins? Damn the Torpedos and Full Speed Ahead

Absolutely Relative
Risk is in the Mind of the Taker

Among the many editorials providing background and backlash about the new cholesterol guidelines is this one:  More Than a Billion People Taking Statins? by John Ioannidis, which echoes the worries of others that the result of the guidelines (which changed the 10-year risk threshold for treatment from 10% to 7.5%) may be that many more people (billions and billions?) will be prescribed statins.  But the title is a curious one - if statins are beneficial, should we lament their widespread prescription and adoption or is it just unfortunate that heart disease is so prevalent? Whose side are we on, the cure or the disease?

Are the premises of the guidelines flawed leading to flawed extrapolations, or are the premises correct and we just don't like the implications?  Let's look at the premises - because if they're flawed, we may find that other premises we have accepted are flawed.