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Diagnostic Sample Size Calculator

Buderer's formula for diagnostic accuracy studies. Compute required sample size from expected sensitivity, specificity, prevalence, and desired CI width. Data never leaves your browser.

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From pilot data or literature

From pilot data or literature

Expected in study population

Total width (e.g., 0.10 = ±5%)

Expected % lost to follow-up

  • Plan a single-arm diagnostic accuracy study (no comparison group)
  • Determine how many diseased and non-diseased subjects you need to achieve target CI width
  • Explore sample size trade-offs across different prevalence and precision scenarios
  • Justify sample size in a grant application or study protocol using Buderer's formula
  • Account for expected dropout when planning enrollment targets

Don't use for

  • Two-group hypothesis testing (comparing treatments) — use a standard power/sample size calculator
  • ROC curve comparison studies — specialized AUC comparison sample size methods are needed
  • Already collected data — use the Diagnostic Test Calculator to analyze your results

Diagnostic Study Sample Size

Standard power analysis (for comparing two groups) does not apply to single-arm diagnostic accuracy studies. Instead, Buderer's formula sizes the study to achieve a desired precision (CI width) for sensitivity and specificity.

The key formula for diseased subjects:

n_diseased = z2\text{z}^{2} ×\times Sens ×\times (1−Sens) / W2\text{W}^{2}

And for non-diseased subjects:

n_healthy = z2\text{z}^{2} ×\times Spec ×\times (1−Spec) / W2\text{W}^{2}

Total N = max(n_diseased/prevalence, n_healthy/(1−prevalence))

This ensures both sensitivity and specificity are estimated with the desired precision.

Frequently Asked Questions

Next Steps

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Diagnostic Test Calculator — Analyze collected data

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ROC/AUC Calculator — Continuous-score threshold analysis

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General Stats Calculator — Two-group comparisons

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