Background: Confidence intervals (or associated standard errors) facilitate assessment of the practical importance of the findings of a health study, and their incorporation into a meta-analysis. For paired design studies, these items are often not reported. Since the descriptive statistics for such studies are usually presented in the same way as for unpaired designs, direct computation of the standard error is not possible without additional information.
Methods: Elementary, well-known relationships between standard errors and p-values were used to develop computation schemes for paired mean difference, risk difference, risk ratio and odds ratio.
Results: Unreported confidence intervals for large sample paired binary and numeric data can be computed fairly accurately using simple methods provided the p-value is given. In the case of paired binary data, the design based 2 × 2 table can be reconstructed as well.
Conclusions: Our results will facilitate appropriate interpretation of paired design studies, and their incorporation into meta-analyses.