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Number Needed to Treat (NNT) Calculator

The **Number Needed to Treat (NNT)** is an effect-size measure introduced by Laupacis, Sackett & Roberts in the *New England Journal of Medicine* in 1988. It answers a direct clinical question: "how many patients must I treat with this intervention to prevent one additional adverse event?" Enter the **control event rate (CER)** — e.g. 5 % of placebo patients die — and the **experimental event rate (EER)** — e.g. 1 % of patients on the new drug die — and the tool returns: (a) **absolute risk reduction ARR = CER − EER**; (b) **relative risk reduction RRR = ARR / CER**; (c) **relative risk RR = EER / CER**; (d) **NNT = 1 / ARR**, rounded up. **When the experimental arm has *more* events** (EER > CER), the tool flips to the **NNH** (Number Needed to Harm = 1 / |ARR|). NNT is widely used in evidence-based medicine, RCT reporting, exam revision and shared decision-making because, unlike relative measures, it reflects the *baseline risk* and is intuitive at the bedside.

Number Needed to Treat

Absolute risk difference (ARR)

Relative risk reduction (RRR)

Relative risk RR (EER / CER)

NNT = 1 / |CER − EER|, rounded up (you cannot treat 3.2 patients). EER < CER → treatment is beneficial → report NNT; EER > CER → treatment is harmful → report NNH. Centre for Evidence-Based Medicine guidance: (a) always pair the point estimate with a 95 % confidence interval; (b) NNTs from different trials are only comparable if the time horizon, outcome definition and population are similar; (c) a large NNT is not the same as "no value" — population-wide screening or vaccine NNTs can run to the hundreds and still matter at the public-health level.

Formula

Inputs are proportions (entered as percentages, used internally as fractions in [0, 1]): CER = control event rate EER = experimental event rate Core measures: ARR (Absolute Risk Reduction) = CER − EER RRR (Relative Risk Reduction) = (CER − EER) / CER RR (Relative Risk) = EER / CER NNT = ⌈1 / |ARR|⌉ (rounded up) Direction: ARR > 0 → treatment reduces events → report NNT (benefit) ARR < 0 → treatment increases events → report NNH (harm) ARR = 0 → no effect Following Altman (BMJ 1998), real reports must pair the point estimate with a 95 % confidence interval — this tool gives the point estimate only.

Frequently asked

What is the difference between NNT and NNH?

**Only the sign of the effect differs — the formula is the same, NNT = NNH = 1 / |ARR|.** (a) **NNT** applies when the treatment *reduces* the event rate (EER < CER) — how many patients need to be treated for one extra benefit. (b) **NNH** applies when the treatment *raises* the event rate (EER > CER) — how many patients need to be treated for one extra harm (a side effect or serious adverse event). **In practice**: (1) efficacy trials with a "good" outcome (survival, cure rate) report NNT; (2) safety analyses with a "bad" outcome (hepatotoxicity, bleeding) report NNH. (3) **The same trial often reports both** — e.g. an antihypertensive might have NNT = 30 (preventing stroke) and NNH = 100 (causing hypokalaemia), a 3.3 : 1 benefit-to-harm ratio. **Trap**: NNT and NNH must share the same time horizon — never compare a 5-year NNT against a 1-year NNH.

Is a smaller NNT always better? Is NNT = 1 000 worth doing?

**Smaller NNT is generally better, but whether it is "worth it" depends on three factors: cost, side effects and the severity of the outcome.** (a) **Smaller NNT = larger absolute benefit per treated patient**, so NNT = 3 (direct-acting antivirals curing hepatitis C) is obviously stronger than NNT = 30 (statin secondary prevention). (b) **NNT = 1 000 can still be worth it** if (1) the prevented event is severe (death, major disability); (2) the treatment is cheap; (3) side effects are rare and mild. **Classic example**: HPV vaccine has NNT ≈ 100 for lifetime cervical-cancer prevention — vaccine cost is small, side effects rare and mild, and the prevented event is potentially fatal — so public-health coverage is justified. **Counter-example**: NNT = 1 000 with frequent serious side effects or huge cost — e.g. some chemotherapy regimens for primary prevention in low-risk people — usually fails the value test. **Framework**: value ≈ (NNT × per-patient cost) ÷ (event severity × event probability). **For shared decision-making**: a 100-icon pictograph showing absolute benefit beats quoting "RRR = 50 %" every time — it gives the patient an intuitive sense of baseline risk and absolute gain.

If the CER is 0 % (no events in the control arm), how is the NNT computed?

**CER = 0 is a boundary case** — RRR and RR are undefined (division by zero), but NNT can still be computed. **What this tool does**: (a) ARR = CER − EER = 0 − EER = −EER (if EER > 0), so the direction is *harm*; (b) the result is shown as NNH = 1 / EER; (c) RRR and RR are shown as "—" (undefined). **Statistical advice**: (1) CER = 0 in a real trial usually means (i) the sample is too small — report a Wilson-score CI to reflect the uncertainty; (ii) the follow-up is too short — increase it; or (iii) the event is genuinely rare — consider Poisson regression instead of RR/OR. (2) **Workarounds**: (a) the Hayes (1988) "continuity correction" adds 0.5 to both numerator and denominator in each arm so the formulas resolve; (b) Bayesian methods with a sensible prior; (c) report the zero-event rate with an exact Clopper-Pearson CI. **This tool** is intended for teaching; for confidence intervals use professional packages such as R `epitools::riskratio()` or `epi.2by2`.

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