RLO Transcript: NNT/NNH
This is a transcript of the Reusable Learning Object (RLO) entitled "Numbers needed to treat (NNT) and Numbers needed to harm (NNH)", online at:

http://www.nottingham.ac.uk/nursing/sonet/rlos/ebp/nnt_nnh

Introduction

New advances in healthcare are introduced everyday and the wealth of information can be overwhelming. 

There are many methods of describing the measure of effectiveness of new interventions in ways that aim to help patients, prescriber’s and policy-makers understand the relative benefits and harms of these treatments and make informed decisions.

One of these methods is to calculate how many patients would need to be treated with a drug, in order for ONE of them to show the desired effect.  This value is known as the numbers needed to treat, or the NNT. 
Numbers needed to treat

Suppose you read about a new drug for treating headaches called Headeeze and you want to determine if the drug has any benefit.  You find a randomised control trial that shows that; in a control group 90 out of 100 people still experienced a headache 2 hours after taking a placebo.  This is known as the control event rate (CER) and is usually expressed as a percentage. 

However, in a group treated with Headeeze the number who still experienced a headache at 2 hours fell to 70 out of 100.  This is known as the experimental event rate (EER) and is again usually presented as a percentage. The difference between the experimental and control event rate is known as the absolute risk reduction (ARR). 

The numbers needed to treat is the reciprocal (or inverse) of the absolute risk reduction. It is calculated by dividing 1 by the absolute risk reduction. As the absolute risk reduction is usually expressed as a percentage, we need to multiply this by 100.

In the example here this would give a value of 5, showing that 5 people would need to be treated with the drug in order for 1 to show a beneficial effect of the drug at 2 hours.
Interpretation

In the example we have used the NNT was calculated as a whole value, but this is rarely the case.  Suppose that in our previous example the experimental event rate had been 60%. The NNT would then be 3.3.  In all cases the NNT is rounded up, so in this case it would be 4.  

In a perfect world an intervention would have an NNT of 1 meaning that everyone would experience the benefit of the drug.  Unfortunately, an NNT of 1 is very rare, but the smaller the NNT, the better the intervention.

So what do you think is an acceptable maximum NNT?  
Use the sliding scale to register your view.

Unfortunately, there are no hard and fast rules as to what is an acceptable value for an NNT. It depends on a number of factors including the severity of the condition being treated, possible side effects and costs, along with individual values and preferences.
Using NNT to compare different drug treatments (Activity)

Although the calculated NNT value is treatment specific it can be used to compare different drug treatments.

Work through the following example to see how we can compare the results that we calculated for Headeeze with those for the established headache treatment Noache.

In a randomised control trial for Noache 88 out of 100 still experienced a headache 2 hours after taking the placebo, whereas only 78 out of 100 still experienced headache in the group taking Noache.

Complete the table to calculate the Absolute risk reduction (ARR)

Now calculate the NNT for Noache. 

You can compare this data with that which we have already calculated and looked at for Headeeze

Based on this data alone, which drug would you choose? (Click on the drug you would choose).

Numbers needed to harm

This way of presenting information about drugs can also be used to express the measure of potential harmful effects of an intervention.  In this case the control event rate and the experimental event rate are calculated as the percentage of individuals in respective treatment groups that show a harmful effect of the intervention.

In this case subtracting the control event rate from the experimental event rate gives us a measure known as absolute risk INCREASE (ARI).  From this the value known as the number needed to harm (NNH) can be calculated by dividing 1 by the absolute risk increase, and again multiplying by 100 when the ARI is expressed as a percentage.  

NNH shows how many individuals would need to be treated with the drug in order for 1 to show the harmful effect.

Suppose both Headeeze and Noache can also cause harm and increase the number of major bleeds in patients. The control and experimental event rates for the incidence of bleeds with Noache and Headeeze is shown in this table.  

The ARI and NNH for Headeeze have already been calculated. The NNH for Headeeze is 13 (i.e. 13 people need to be treated with Headeeze for 1 person to experience a bleed / harm.

Calculate the absolute risk increase and the numbers needed to harm for Noache.

Does this change your view of which drug to use? (Click on the drug you would choose).

It is important to balance the benefits of an intervention with the potential harms and incorporate both in the decision making process.
Resources
Title/link
	Description
	
Relative Risk Reduction and Absolute Risk Reduction (RLO)	This RLO considers how to measure and interpret the magnitude of effect in clinical trial results using relative risk reduction (RRR) and absolute risk reduction (ARR).
	
Determining the clinical importance of trial results (RLO)	Demonstrates how to interpret and use clinical trial data (ARR, RRR, NNT, NNH, and confidence intervals) in practice.
	
Surrogate Outcomes (RLO)	Considers the type of evidence which should be used when making decisions about patient care.
	
Sensitivity and Specificity (RLO)	Explains how diagnostic test accuracy is described by the terms sensitivity and specificity. Sensitivity describes the accuracy of the test in detecting disease. Specificity describes the accuracy of the test in detecting health.
	
Confidence Intervals (RLO)	Defines the term 'confidence intervals' and demonstrates how they can be used to determine the significance and range of possible sizes of a treatment effect.
	
Barratt A et al. Tips for learners of evidence-based medicine: 1. Relative risk reduction, absolute risk reduction and number needed to treat. CMAJ 2004;171 (4):353-358

Watt E, Burrell A. Implementing NNTs. Volume 1, number 7 available at www.evidence-based-medicine.co.uk 

Glossary

Control event rate (CER) = proportion of patients who experience an outcome in the control group

Experimental event rate (EER) = proportion of patients who experience an outcome in the experimental group

Relative risk reduction (RRR) = difference in event rates relative to (or proportional to) the control event rate, expressed as a percentage 
                                       RRR = (CER – EER) / CER

Absolute risk reduction (ARR) = difference between the control event rate and the experimental event rate, expressed as a percentage
                                        ARR = CER – EER

Number needed to treat (NNT) = the number of patients that would need to be treated in order for ONE on them to have the beneficial outcome.
It is calculated by calculating the reciprocal (or inverse) of the ARR
                                       NNT = 1 / ARR
Note: if the ARR is expressed as a percentage (%) then:
                                       NNT = 1/ARR x 100 
                                              or 100/ARR 

Number needed to harm (NNH) = the number of patients that would need to be treated in order for ONE on them to experience the adverse outcome. 
It is calculated by calculating the reciprocal (or inverse) of the absolute risk increase (ARI)
                                       NNH = 1/ARI
Note: if the ARI is expressed as a percentage (%) then:
                                       NNH = 1/ARI x 100
                                               or 100/ARI 
Tables
The tables below are referred to in the above transcript:

Noache results

Table of results

Benefits:	Placebo	Noache	ARR	NNT	
Event rate (Presence of headache in 2 hours)	
88%	
78%	
10%
(88% - 78%)	
10
(1 / 10%)
Or (100/10)	

Comparison with Headeeze results:

Benefits:	Placebo	Headeeze	ARR	NNT	
Event rate (Presence of headache in 2 hours)	
90%	
70%	
20%
(90% - 70%)	
5
(1 / 20%)
Or (100/20)	

Explanation box appears ‘Based on these results the new drug Headeeze looks more effective at relieving headaches as only 5 patients need to be treated with Headeeze to relieve a headache in 2 hours in 1 patient. In contrast, 10 patients need to be treated with Noache in order to relieve a headache in 1 patient’.

Results table for harm for both Headeeze and Noache

Benefits:	Placebo	Headeeze	ARR	NNT	
Event rate (Presence of headache in 2 hours)	
90%	
70%	
20%
(90% - 70%)	
5
(1 / 20%)
Or (100/20)	
Harms:	Placebo	Headeeze	ARI	NNH	
Event rate (Suffered major bleed)	
1%	
9%	
8%
(9% - 1%)	
13
(1 / 8%)
Or (100/8)	

Benefits:	Placebo	Noache	ARR	NNT	
Event rate (Presence of headache in 2 hours)	
88%	
78%	
10%
(88% - 78%)	
10
(1 / 10%)
Or (100/10)	
Harms:	Placebo	Noache	ARI	NNH	
Event rate (Suffered major bleed)	
2%	
5%	
3%
(5% - 2%)	
34
(1 / 3%)
Or (100/3)	

Explanation box appears ‘The decision is not so clear now. Although Headeeze is more effective at relieving headaches than Noache, it also has a higher risk of causing a major bleed than Noache. Other factors that will influence the decision making process include the individual drug costs and the further issues with managing a major bleed, in addition to the individual patient values and preferences.’

If you treat 100 patients with Headeeze, then 30 patients will get relief of their headache in 2 hours. But 8 of the 100 patients will suffer a major bleed.
If you treat 100 patients with Noache, then only 10 patients will get relief of their headache in 2 hours. But only 3 of the 100 patients will suffer a major bleed.