Number needed to treat – one way to frame harms vs. benefits: An update to our toolkit

Michael Joyce is a writer-producer with and tweets as @mlmjoyce

A key to making an informed medical choice is weighing benefits and harms.

But how do you know how likely you are to benefit from a medical treatment or procedure?

Number needed to treatIn making these decisions it’s not always easy to decipher the latest research because clinical trial results on drugs and other interventions are communicated in a variety of ways such as P values, odds ratios, and relative or absolute risk. Often times we’re told a study is statistically significant, but we’re left wondering if it’s really clinically significant.

That’s where number needed to treat, or NNT, comes in.

It provides another way to frame the impact of a treatment; more specifically, it tells us how many people need to receive a medication or intervention for just one person to benefit (or to avoid one adverse outcome).

A real world example

Back in the mid 90s the cholesterol-lowering drug, Lipitor, was released to much fanfare. Advertisements claimed it reduced the risk of heart attacks by 36%. But that was simply the relative risk reduction, a statistic often favored in ads because it’s more eye-catching.

As you can imagine the 36% figure was enticing; so much so that for the next 15 years or so Lipitor became the world’s best-selling drug of all time (up to that point).

But astute media coverage pointed out the absolute risk reduction (ARR) was closer to 1%.

The NNT is simply the inverse of the ARR. In this case the NNT is 100 / 1 = 100. Now it’s possible to frame the drug’s benefits in a whole new way. That is, 100 people would need to be treated with Lipitor for just 1 person to benefit.

Put another way, 99 people would need to take the drug, pay for it, run the risk of side effects, and stand no chance of benefit. Of course, no one knows going in who will be that lucky 1 out of 100 who does benefit.

New expanded toolkit primer

To help our readers make sense of this statistical concept we’ve updated and expanded our toolkit with this primer:

Number Needed to Treat (NNT): A tool to analyze harms and benefits

In it you’ll learn:

  • How and when to use the NNT.
  • Clinical scenarios showing how the NNT can help patients facing treatment decisions.
  • News stories we’ve reviewed in which the NNT was or wasn’t used, and why this mattered.
  • Why some critics don’t like the NNT.
  • More on the related concepts of Number Needed to Harm (NNH) and Number Needed to Screen (NNS).

You can find more tips for analyzing studies and health care claims in our TOOLKIT section.

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