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Researchers increasingly use meta-analysis to synthesize the results ofseveral studies in order to estimate a common effect. When the outcome variableis continuous, standard meta-analytic approaches assume that the primarystudies report the sample mean and standard deviation of the outcome. However,when the outcome is skewed, authors sometimes summarize the data by reportingthe sample median and one or both of (i) the minimum and maximum values and(ii) the first and third quartiles, but do not report the mean or standarddeviation. To include these studies in meta-analysis, several methods have beendeveloped to estimate the sample mean and standard deviation from the reportedsummary data. A major limitation of these widely used methods is that theyassume that the outcome distribution is normal, which is unlikely to be tenablefor studies reporting medians. We propose two novel approaches to estimate thesample mean and standard deviation when data are suspected to be non-normal.Our simulation results and empirical assessments show that the proposed methodsoften perform better than the existing methods when applied to non-normal data.

Estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis