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For a beneficial intervention reducing an adverse-event risk from $p_C$ to $p_I$ over a stated follow-up period, the number needed to treat is the reciprocal of the absolute risk reduction. Its clinical interpretation requires the population, outcome, horizon and comparison to be specified. Invert confidence limits for the risk difference in reversed order if both are positive. If the interval includes zero, the transformed confidence set is unbounded and may include both benefit and harm, rather than being one ordinary finite interval.

The number needed to treat is the reciprocal of the absolute reduction in the 12-week adverse-outcome risk. Using the counts rather than rounding the percentages first givesThis means about nine high-risk women would need to be offered the intervention to prevent one additional screen-defined case over that follow-up, under the trial's comparison. It is not a lifetime quantity or automatically an estimate for clinical-interview diagnoses. Calculating from the rounded difference $25\%-14\%$ gives approximately nine but need not give the same integer rounding; the exact counts explain the reported value.

For a confidence interval, first construct an interval for the difference between the two independent event proportions, allowing for binomial distribution sampling uncertainty. If both risk-reduction endpoints are positive, take their reciprocals and reverse their order to obtain an NNT interval. A bootstrap can instead resample women within their randomized controlled trial arms and transform the resulting risk-difference distribution. If the difference interval includes zero, the NNT confidence set extends through infinity and can include harm as well as benefit; it is not an ordinary bounded interval. The raw-outcome interval also relies on the missing-outcome assumptions discussed above.