Are the results of this study important?
What is the magnitude of the treatment effect?
There are several ways that information about treatment effects can be presented. This discussion will be illustrated using the results of NASCET (for any stroke at 5 years) as shown in the first row of numbers in the table below.
|Control Event Rate||Experimental Event Rate||Relative Risk Reduction||Absolute Risk Reduction||Number Needed to Treat|
The control event rate (CER) is the proportion of patients in the control group (in this study, the group that received medical care) that had the outcome event of interest (in our scenario, this would be any stroke). The experimental event rate (EER) is the proportion of patients in the experimental group (patients in the carotid endarterectomy group) that had the outcome of interest.
The relative risk reduction (RRR) is one way of describing the treatment effects and is calculated as:
RRR = |EER-CER|/CER
RRR = |0.198-0.264|/0.264
RRR = 25%
Applying this, we can say that if we treat people who have moderate carotid stenosis with carotid endarterectomy we can decrease their risk of future stroke by 25% compared to those people who receive medical therapy only.
If the experimental treatment increases the risk of a good event, we can use this same equation to calculate the relative benefit increase (RBI). Similarly, if the experimental treatment increases the risk of an adverse event we can use the equation to calculate the relative risk increase (RRI).
The RRR has limitations. Consider the second row of numbers in the table above - when the CER was incredibly small (0.000000264) the RRR remains at 25%. The RRR is unable to discriminate between small treatment effects and large ones and doesn't reflect the baseline risk of the event.
One measure that overcomes this is the absolute difference between the CER and EER or the absolute risk reduction (ARR). It is calculated as:
ARR = |EER-CER|
ARR = |0.198-0.264|
ARR = 0.066
If the experimental treatment increased the risk of a good event, we can use this same equation to calculate the absolute benefit increase (ABI). Or, if the experimental treatment increases the risk of an adverse event, we can use the equation to calculate the absolute risk increase (ARI).
Returning to the data in the table, we can see that the ARR reflects the baseline risk of the event and that it discriminates between small and large treatment effects. However, because it is not a whole number, it is often difficult to remember and to translate to patients.
To overcome these difficulties, we can take the inverse of the ARR which tells us the number of patients that we'd need to treat with the experimental therapy in order to prevent one additional bad event. This is called the number needed to treat (NNT) and in our example, the NNT is 15. We can see from the table that the NNT (like the ARR) is able to differentiate between small and large treatment effects - in the second row of the table, when the CER and EER are very small, the NNT is over 15 million!
When the treatment increases the risk of adverse events, we can calculate the number of patients that we'd need to treat with this therapy to cause one additional bad event and this term is called the number needed to harm (NNH). The NNH is calculated as 1/ARI.
How big should an NNT be for us to be impressed? Consider some examples. We'd need to treat 40 people who have suspected MI with aspirin to prevent 1 additional death. And, we'd only need to treat 20 people who have suspected MI with aspirin and thrombolysis to prevent 1 additional death. If you want to see more examples of NNTs, please click here.
What is the precision of the treatment effect?
Calculate the confidence level for the NNT online!
Try it now!
The confidence interval around the NNT can be calculated as the inverse of the confidence interval for the ARR. The smaller the number of patients who have the event of interest, the wider the confidence interval.