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Confidence interval from a p-value
Pubrica’s free calculator recovers the confidence interval for an estimate when a paper reports only the estimate and its p-value. It applies Altman and Bland’s reverse calculation and provides the interval data needed for meta-analysis and critical appraisal.
The tool runs in your browser. No registration, email address or file upload is required, and the formula, reference or assessment criteria used are shown with the result.
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The Tool
Calculate a Confidence Interval from an Estimate and P-Value
Calculates the confidence interval for a published estimate when a paper reports only the estimate and its p-value.
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Calculates the confidence interval for a published estimate when a paper reports only the estimate and its p-value.
Altman & Bland reverse calculation0.066
Lower limit
0.680
Estimate as published
1.294
Upper limit
This is the interval the paper should have printed. It is worth doing whenever a meta-analysis needs a variance and the source reports only an estimate and a p-value — and whenever a claim rests on a p-value alone, because the width of this interval usually settles the argument faster than the p-value does.
Method. Altman and Bland’s reverse calculation (BMJ 2011;343:d2090): z = Φ⁻¹(1 − p/2), SE = estimate / z, then estimate ± zcrit·SE. Ratio measures are worked on the log scale and exponentiated back. It assumes the published p-value is two-sided and came from a normal or large-sample test; for a small sample or an exact test the recovered interval is approximate.
Limitations
What This Tool Does Not Cover
Use the result as a starting point. These are the situations in which it needs specialist review.
The calculators
Limitations of the calculators
The sample size calculator gives the closed-form answer for two independent groups. Cluster randomisation, interim analyses, a non-inferiority margin, repeated measures and competing risks each change the number materially, and none of them can be handled by inflating the answer afterwards. The same applies to the test chooser: it cannot know that your observations are clustered within clinics, and clustering overrides almost everything else it would tell you.
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