Benjamin Franklin said nothing in this world except death and taxes is certain. Of course, there are other some other absolutes, like absolute zero — theoretically, the lowest possible temperature — when particles nearly stand still. And while outcomes can be expressed in absolute and relative terms, one thing is certain: Payers prefer absolute measures of risk reduction over relative measures.
Absolute measures
Suppose a new technology — a drug, device, or diagnostic — reduces the risk of an adverse outcome. Absolute risk reduction (ARR) is the proportion of patients who avoid the adverse outcome when this new technology is used. Take, for example, a study comparing patients who have access to a new technology (the experimental group) with patients who do not (the control group). If 8% of patients in the experimental group experience a specific adverse outcome and 10% of patients in the control group experience the same bad outcome, then the ARR is 2% — that is, 2 patients of 100, if treated with the new technology, would avoid the adverse outcome. In this example, the number needed to treat (NNT) is 50 (100/2), meaning that 50 patients would have to be treated using this technology to avoid one patient from experiencing the adverse event.
Payers are responsible for managing the care of populations, and absolute measures help them translate benefits and costs into population terms and quantify the return on their investments. These measures are easy to calculate and easy for payers to interpret when making market-access decisions. Payers will weigh the total costs of the technology against its total benefits and are increasingly using the cost per NNT as a means to compare the cost-effectiveness of technologies. Despite the concrete and population-based nature of absolute measures, thorny issues related to perceptions of practical significance (clinical or economic) and value are inescapable.
By the way, the same approach is used to calculate the NNT when comparing two specific technologies (in this case, the “control” group becomes a “comparison” group). This approach is also the same when calculating the number needed to screen (NNS), and very similar to the approach used to calculate the number needed to harm (NNH). NNH may be particularly relevant, as no technology is risk-free and some are quite risky. Bad things can happen, even when the intent is good.
Relative measures
In our example, the relative risk of experiencing an adverse event using this technology versus not using it is 0.8 (8/10), making the relative risk reduction (RRR) 20% (1 – 0.8). Yes, this new technology lowers risk. The manufacturer might triumphantly, and accurately, report that the RRR is 20%. If you think this sounds impressive, consider that payers know that this is not the actual magnitude of the effect; after all, the ARR in our example was only 2%.
This is where NNT comes in. The NNT to achieve an RRR of 20% could be 5, 50, 500, or 5,000 people (or fewer or more or anything in between). But without additional information, such as ARR, NNT, or the underlying adverse event rates, payers can’t interpret relative measures. When outcomes are described only in relative terms, a benefit that may not be clinically or economically significant can appear deceptively large.
If manufacturers do not supply all of the relevant information, payers will acquire or estimate any additional information they need to contextualize the performance of the newer technology — then they will calculate the absolute measures they want and need to make market-access decisions.
A chilling effect
Curiously, despite payers’ clear preference for absolute measures, some manufacturers promote only relative measures when marketing to payers. When marketing to payers, do these manufacturers actually believe such communications are effective? Or do they simply grab the metric they think sounds most impressive without consideration of impact? Unfortunately, tactics like this can have a chilling effect on payer relations and reinforce payer perceptions that manufacturers often withhold or distort information.
The best strategy is for manufacturers to warm relations with payers by providing them with the information they need and want. That’s what payers are ultimately going to get and use to make market-access decisions — with or without the help from manufacturers.
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