Advanced Analytics Illustrate Potential Benefits and Harms from Medical Treatments
Every form of medical therapy has a Number Needed to Treat (N.N.T.) and Number Needed to Harm (N.N.H.). These statistics help patients learn whether a treatment is worth pursuing.
Most of my life experience and lessons learned can be filtered through baseball metaphors. I'm about to employ one here. Forty years ago, the casual baseball fan was able to glean a cursory understanding of the game's nuances through a mixture of watching the action and reading up on basic player statistics. Beginning in the 1980s and picking up major steam in the early 2000s, the sabermetrics revolution offered baseball aficionados a higher level of insight into the game's intricacies. This was made possible through access to better data and advances in stats analysis. The intangible became tangible. The invisible became alive.
We've seen similar advances in rational analytics in fields like business, education, and medicine, to name just a few. In each case the ability to collect, analyze, and communicate data samples saw marked growth. The layperson was given access to the clues necessary for building informed opinions.
Today's edition of The New York Times features an article authored by health policy experts Aaron E. Carroll and Austin Frakt. Part of a series on medical analytics, Carroll and Frakt's piece explores the concepts of Number Needed to Treat (N.N.T.) and Number Needed to Harm (N.N.H.):
"N.N.T. tells you the number of people who would need to receive a medical therapy in order for one person to benefit. N.N.T.s well above 10 or even 100 are common. But knowing the potential for benefit is not enough. We must also consider potential harms.
Not every person who takes a medication will suffer a side effect, just as not every person will see a benefit. This fact can be expressed by Number Needed to Harm (N.N.H.), which is the flip side of N.N.T."
Just as you can now analyze how many times a baseball batter can strike out yet still be considered a good player, N.N.T. and N.N.H. offer insight into the relative risks and reward of various medical treatments. Carroll and Frakt demonstrate this using aspirin as an example:
"The N.N.T. for aspirin to prevent one additional heart attack over two years is 2,000. Even though this means that you have less than a 0.1 percent chance of seeing a benefit, you might think it’s worth it. After all, it’s just an aspirin. What harm could it do?
But aspirin can cause a number of problems, including increasing the chance of bleeding in the head or gastrointestinal tract. Not everyone who takes aspirin will bleed. Moreover, some people will bleed whether or not they take aspirin.
Aspirin’s N.N.H. for such major bleeding events is 3,333. For every 3,333 people, just over two on average will have a major bleeding event, whether they take aspirin or not. About 3,330 will have no bleed regardless of what they do. But for every 3,333 people who take aspirin for two years, one additional person will have a major bleeding event. That’s an expression of the risk of aspirin, complementing the fact that one out of 2,000 will avoid a heart attack."
I recommend taking a look at their full piece and the links within that lead to their full series of articles. What'll be most interesting here (and what I think is the key takeaway) is that the analytics revolution has the potential to make us more knowledgeable participants in nearly all facets of life. This isn't to say we should assume we can all of a sudden become overnight medical experts; the legion of idiots rallying against vaccinations is evidence of that. But gaining insight by way of real facts is almost never a bad thing.
Read more at The NY Times.
Photo credit: piotr_pabijan / Shutterstock
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