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The number needed to treat (NNT) is the number of patients Patients Individuals participating in the health care system for the purpose of receiving therapeutic, diagnostic, or preventive procedures. Clinician–Patient Relationship that are needed to treat to prevent 1 additional adverse outcome (e.g., stroke, death). For example, if a drug has an NNT of 10, it means 10 people must be treated with the drug to prevent 1 additional adverse outcome. The NNT is the inverse of the absolute risk reduction Absolute risk reduction Measures of Risk (ARR), which is equal to the rate of adverse outcomes occurring in the control group minus the number of adverse outcomes in the experimental group.
Last updated: Aug 11, 2022
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In order to comprehend the concept of number needed to treat, some previous knowledge about descriptive and inferential statistics is recommended.
The number needed to treat (NNT), also called the number needed to benefit (NNTB); and its analog, the number needed to harm (NNH), are simply other measures of effect sizes, like Cohen’s d Cohen’s d Cohen’s d is the most common (but imperfect) method to calculate ES. Cohen’s d = the estimated difference in the means/(pooled estimated standard deviations). Statistical Power, and help relate an effect size Effect size Effect size is the standardized mean difference between 2 groups, which is exactly equivalent to the “Z-score” of a standard normal distribution. Statistical Power difference back to real-world clinical relevance.
The NNT signifies how many patients Patients Individuals participating in the health care system for the purpose of receiving therapeutic, diagnostic, or preventive procedures. Clinician–Patient Relationship would need to be treated to get 1 additional patient better, who would not have otherwise gotten better without that particular treatment.
All terms represent the absolute value of the difference between the proportion (expressed as a percent, fraction, or incidence Incidence The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from prevalence, which refers to all cases in the population at a given time. Measures of Disease Frequency) of patients Patients Individuals participating in the health care system for the purpose of receiving therapeutic, diagnostic, or preventive procedures. Clinician–Patient Relationship in the control group (Pc) who had the outcome of interest and the proportion of patients Patients Individuals participating in the health care system for the purpose of receiving therapeutic, diagnostic, or preventive procedures. Clinician–Patient Relationship in the experimental group (Pe) with that the outcome of interest:
$$ {ARR = ARD = ARE = \left | P_{c} – P_{e} \right |} $$The NNH is the additional number of individuals who need to be exposed to risk (harmful exposure or treatment) to have 1 extra person develop the disease compared to that in the unexposed group.
A 2 x 2 contingency table Contingency table A contingency table lists the frequency distributions of variables from a study and is a convenient way to look at any relationships between variables. Measures of Risk uses a binary outcome and 2 groups of subjects to show the basis for calculating NNT and NNH. Each result must be expressed as a proportion, percent, or incidence Incidence The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from prevalence, which refers to all cases in the population at a given time. Measures of Disease Frequency, and not as the actual number of subjects.
Outcome | Treated group | Control group |
---|---|---|
Positive | a | b |
Negative | c | d |
Total | a + c | b + d |
If the following is true, the difference in proportions is P treated – P control.
The absolute risk Absolute risk The AR is the risk of developing a disease or condition after an exposure. Measures of Risk difference (ARD) is equal to the ARR, which is calculated as the absolute value of the difference between P treated and P control.
$$ {ARD = ARR = \left | P_{treated} – P_{control} \right |} $$So, the NNT can be calculated as:
$$ {NTT = \frac{1}{\left | P_{treated} – P_{control} \right |}} $$If the treated or exposed group has a worse outcome than the control, then the ARR is called ARE. In that case, the NNT is called the number needed to harm (NNH). In both cases, the calculation is the same (NNH = 1/ARD).
A randomized clinical trial studied the effect of childhood exposure to 2nd-hand smoke on the incidence Incidence The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from prevalence, which refers to all cases in the population at a given time. Measures of Disease Frequency of bronchogenic adenocarcinoma (BA). The study included 100 subjects (50 exposed to childhood 2nd-hand smoke and 50 healthy controls with no childhood exposure) and involved monitoring the lifetime incidence Incidence The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from prevalence, which refers to all cases in the population at a given time. Measures of Disease Frequency of BA. Data from the study are shown in the table below:
Outcome | Exposed group | Control group |
---|---|---|
BA present | 18 | 7 |
BA not present | 32 | 43 |
Total | 50 | 50 |
What is the NNH?
Answer: NNH = 1/ absolute risk Absolute risk The AR is the risk of developing a disease or condition after an exposure. Measures of Risk difference (called “ARE” when NNH is involved). ARE = Pe – Pc = 18/50 – 7/50 = 0.22. NNH = 1/0.22 = 4.45 ⇾ 5, which means that 5 individuals need to be exposed to childhood 2nd-hand smoke to have 1 extra person develop BA compared to that in the unexposed group.
What is the relative risk increase Relative risk increase Measures of Risk in the study cited in Question 1?
Answer: The relative risk increase Relative risk increase Measures of Risk = (Pe – Pc)/Pc = (18/50 – 7/50)/7/50 = 1.57, which means that individuals exposed to childhood 2nd-hand smoke are 1.57 times more likely to develop BA after exposure to 2nd-hand smoke than those who were not exposed.