## Getting NNTs |
||

- Calculating NNTs
- from raw data - use the formula
- from published odds ratios - use the Table
- from published relative risk - use the nomogram
- L'Abbé plots
- Using NNTs
## Type & Strength of Efficacy Evidence- Strong evidence from at least 1 systematic review of multiple well-designed randomised controlled trials
- Strong evidence from at least 1 properly designed randomised controlled trial of appropriate size
- Evidence from well designed trials without randomisation, single group pre-post, cohort, time series or matched case-controlled studies
- Evidence from well-designed nonexperimental studies from more than 1 centre or research group
- Opinions of respected authorities, based on clinical evidence, descriptive studies or reports of expert committees
## Output from systematic reviewsThe evidence provided in systematic reviews can take various forms. Often it is statistical - an odds ratio, relative risk, hazard ratio or effect size. These show statistical superiority of one treatment over another, or over no treatment, but they are a bit difficult when we try and relate them to clinical practice. Bandolier has favoured the number-needed-to-treat [1] as a useful way of looking at results of reviews or trials for at least two reasons. It is easy to calculate, and provides the treatment-specific result in a form which we can handle. Using NNTs is a bit trickier. Bandolier has started thinking of the NNT in two ways.## Black bag evidenceFirstly - and this is where most systematic reviews are useful - it can help us to make decisions between treatment options. If the NNT for treatment A is lower (better) than treatment B, then, all other things being equal, choosing A over B makes sense. Here the choice is what to put in the black bag. A would go into the black bag, B would not.The other way to use an NNT is when you make choices for an individual patient, perhaps whether to treat or not. The choice here is whether or not to take A out of the black bag and use it. There are, of course, many nuances to all this. Bandolier recommends a new book from David Sackett & colleagues - Evidence-based Medicine: how to practice and teach EBM - as a cheap and worthwhile acquisition for any thinking doctor, nurse, scientist or manager in the NHS [2]. Readers who find NNTs helpful, but who are not entirely comfortable with them, have asked Bandolier to work through some examples of how to obtain and use NNTs. To do this from existing reviews can be difficult, since not all the information is to hand. So Bandolier has done its own systematic review comparing proton pump inhibitors (PPIs) and H2-antagonists (H2As) in the short-term healing and long-term maintenance of reflux oesophagitis. The full text of the review and associated tables and graphs is available on the Bandolier Internet pages. The information from that review will be used to show how NNTs can be calculated in different ways. NNTs can be calculated from raw data using a formula, from odds ratios, or from relative risk reduction and expected prevalence. ## NNT can be calculated by- raw data - use the formula
- published odds ratios - use the Table
- published relative risk - use the nomogram
## 1. Calculating NNTsThe NNT calculation is given below. |

We need to distinguish between
treatments
, such as aspirin as an analgesic, and
preventative measures
, such as aspirin preventing further cardiac problems after myocardial
infarction. Using the number outputs from systematic reviews is different
depending on which you are looking at. The distinction is between treatment and
prophylaxis. For prophylaxis, where fewer events occur in the treated group, the
calculation shown will produce negative NNTs. You can use those (the number will
be correct), or you can switch the active and control groups around to provide
NNTs with a positive sign.
The NNT for prophylaxis is given by the equation 1/(proportion benefiting from control intervention minus the proportion benefiting from experimental intervention), and for treatment by 1/(proportion benefiting from experimental intervention minus the proportion benefiting from control intervention). NNTs for treatment
should be small. We expect large effects in small numbers of people. Because few
treatments are 100% effective and because few controls - even placebo or no
treatment - are without some effect, NNTs for effective treatments are usually in
the range of 2 - 4. Exceptions might be antibiotics. The NNT for Helicobacter
pylori eradication with triple or dual therapy, for instance, is 1.2 (
Bandolier 12
).
NNTs for prophylaxis
will be larger, few patients affected in large populations. So the difference
between treatment and control will be small, giving large NNTs. For instance, use
of aspirin to prevent one death at five weeks after myocardial infarction had an
NNT of 40 (Bandolier 17).
## Using absolute risk reductionThe absolute risk reduction (ARR) is the difference between the event rate in the experimental group and the event rate in the control group. It is the denominator in the NNT calculation. Many reviews and trials provide this information, so if you have it and convert it into a proportion, then you can get the NNT by dividing 1 by the ARR:NNT = 1/ARR ## Confidence IntervalsThe 95% confidence intervals of the NNT are an indication that 19 times out of 20 the 'true' value will be in the specified range. An NNT with an infinite confidence interval is then but a point estimate; it includes the possibility of no benefit or harm. It may still have clinical importance as a benchmark until further data permits finite confidence intervals, but decisions must take this into account. A method for calculating confidence intervals was given in Bandolier 18.## 2. Using odds ratiosWhen it is legitimate and feasible to combine data the odds ratio is the accepted statistical test to show that the experimental intervention works significantly better than control. If a quantitative systematic review produces odds ratios but no NNTs, you can derive NNTs from the Table [3].## Working out the NNT from a published odds ratio (OR)
Odds ratios are on the top line and control event rates (CER) down the left hand side. NNTs are in the boxes. So if you have an odds ratio (eg 0.6) and a CER (eg 0.5), then the NNT will be found where they cross (NNT = 8). A caveat here is that odds ratios should be interpreted with caution when events occur commonly, as in treatments, and odds ratios may over-estimate the benefits of an effect when event rates are above 10%. Odds ratios are likely to be superseded by relative risk reduction because relative risk reduction provides better information in situations where event rates are high [3, 4]. ## 3. Relative risk reductionChatelier and colleagues published a useful NNT nomogram in the BMJ last year [5]. Relative risk reduction - the percentage reduction in risk between the experimental and control group - is used to calculate the NNT for any group in whom the risk of an event happening was known.This is probably most likely to be used in prophylaxis. If you have a review or paper which gives a RRR (in percent) and you know the susceptibility of your patient for a bad outcome (usually called the 'patient expected event rate', or PEER), then you can find out the NNT of an intervention. RRR is calculated by dividing the difference between the rate of events in experimental and control group by the rate of events in the control group. So if 10% of patients have a bad event in controls, and only 9% with some intervention, the RRR is (10-9)/10 = 10%. Relative risk reductions happen in prophylaxis. With treatments we have relative risk increase because we expect more good events. The method works either way. |

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