Treatment Efficacy

The Treatment Efficacy calculator compares event rates between a treatment and a control group — for example, the proportion of patients who experienced a stroke in a drug arm vs. a placebo arm.

Step 1 — Choose the outcome direction

Outcome direction When to use Example
Higher rates are better for patients The event is a good outcome Treatment response, recovery rate
Lower rates are better for patients The event is a bad outcome Mortality, stroke, adverse events

Step 2 — Choose the hypothesis type

Hypothesis type What it tests
Superiority Show that treatment is better than control
Equivalence Show that treatment is equivalent to control
Non-inferiority Show that treatment is not significantly worse than control

For Superiority, set a Superiority threshold (default 5%). For Non-inferiority, set a Non-inferiority margin.

Step 3 — Enter your data

Four numbers:

  • Treatment Events / Total
  • Control Events / Total

Also supports Sample Data Generator and Raw Data Import from Excel.

Step 4 — Calculate

Results panel shows:

  • Treatment/Control proportions (%)
  • Relative Risk (RR) with 95% CI
  • Absolute Risk Reduction (ARR) with 95% CI
  • Relative Risk Reduction (RRR) with 95% CI
  • Odds Ratio (OR) with 95% CI
  • NNT or NNH with 95% CI
  • Chi-square test with p-value
  • Fisher's exact test with p-value
  • Statistical decision aligned with chosen hypothesis type

Step 5 — Sample Size Assessment

Evaluates whether your sample size is adequate. A Decision Explanation block walks through the hypothesis test logic.

A UniversalChatBot is available for follow-up questions.

Statistical methods used

Relative Risk (RR)

Component Formula
RR p_T / p_C
SE of log(RR) sqrt((1-p_T)/events_T + (1-p_C)/events_C)
95% CI exp(ln(RR) +/- 1.96 * SE_logRR)

Absolute Risk Reduction (ARR)

ARR = |p_C - p_T| with Wald SE and 95% CI.

Odds Ratio (OR)

Woolf method: OR = (a*d)/(b*c) with log-scale CI.

NNT / NNH

NNT = 1 / ARR. Labelled NNT (beneficial) or NNH (harmful) based on outcome direction.

Chi-square test

Standard 2x2 chi-square without Yates' correction. df = 1.

Fisher's exact test

Hypergeometric point probability on the 2x2 table.

Hypothesis designs

Design Decision rule
Superiority Treatment effect > threshold
Equivalence Effect lies within equivalence margins
Non-inferiority Effect not worse than margin