John Mack of Pharma Marketing blog discusses a BusinessWeek article entitled "Do Cholesterol Drugs Do Any Good?" In these discussions, they reprint a table giving the estimated NNTs for various drugs, including atorvastatin (Lipitor™, Pfizer). I've reprinted (without permission, but hey it's circulating all over the globe) below:
Mack, like any good marketing guy, sensationalizes the findings in this tables by calling this "the statin lottery." We are treated to an attempt at an explanation of the NNT:
250 people are recruited to participate in the contest. Each person gives me $1,000 and after 1 year one person in the group--selected at random--will receive $250,000 (250 people x $1,000). I keep the interest earned.
I'm not really clear what this analogy has to do with the NNT, but call me skeptical of the lottery analogy and even of the following statement by Dr. Nortin Hadler:
Anything over an NNT of 50 is worse than a lottery ticket; there may be no winners
Both the images of lottery and the statement basically claim that there is not benefit to taking statins. But I argue differently. These arguments against statins are based on bamboozling readers with big numbers, ignoring the payoff of taking statins vs. costs, and ignoring the fact that the use of statins is a very personal decision to be make carefully.
So, on to big numbers. I don't know why Dr. Hadler picked 50 as a cutoff for NNT. He may have given a reason in the interview that wasn't reported (or maybe I missed it), or maybe he just picked a number out of the rectal database. Given that the NNT is tied to a specific clinical outcome, course of treatment (including dose and frequency), and other specific medical events the analogy with lotteries break down. Never mind the fact that lotteries typically have winning chances of less than 1 in a million. So the 1 in 50 number just seems arbitrary. After the quote, we are shown higher NNTs for statins (70-250 and 500+), and have the upper range of that singled out for discussion. Why not discuss 70? Why not discuss the harmonic mean 109 (1/(1/70 + 1/250)), which is probably the right NNT estimate assuming that 70-250 is a confidence interval? Not impressive enough, I guess.
In light of the payoffs, I wonder if 1 in 70-250 even looks so bad. What is the balance of avoiding a freakin' myocardial infarction vs. taking 5 years of statins (assuming you have high blood pressure). Most of the cardiovascular events have a high cost in terms of money, healthcare resources, stress, and lifestyle modifications. What is the tradeoff between taking 5 years of statins (including chance of adverse events and money) and cardiovascular events? For each individual person, I don't know. How about 1 in 500 to avoid death or other "serious medical events" (presumably more serious than a myocardial infarction)? That's something to decide with a doctor. What is the NNT of avoiding MIs in people who have a family history of heart disease or other risk factors more than hypertension?
And the NNT can be a very useful statistic to use in that decision, as long as it is considered in context. Notice in the table there are 3 NNTs associated with statins, depending on the risk factors and the events to avoid. There's more NNTs not listed in the table. And, for context, we are given antibiotics for H. pylori ulcers and Avandia. The reason these are singled out for the table is not given, and it would have been very easy to give a simple chart of NNTs for many common medications. Statins might have looked as bad, worse, or better. It all depends on the context. The risk factors that are intimately tied in with NNTs are not discussed beyond people who have had a heart attack and people who merely have high blood pressure. For example, history of heart disease is not discussed. Finally, the uncertainty in calculating an NNT needs to be acknowledged by showing a confidence interval.
In short, I really appreciate that BusinessWeek discussed the NNT statistic. It is definitely a useful and easily interpretable figure that can be used in medical decision making. However, the simplifying explanations given leave out some useful and necessary information on how exactly to use that statistic to make medical decisions both on a personal and a policy level. I do understand the fact that we are overmedicated, but I also believe it is better to understand the phenomenon and base our course of action on reality than sensationalize it and feed the counterproductive pharma-bashing frenzy.