It is common that studies do not report sufficient data to allow meta-analysis of continuous outcomes. The standard error (SE) of the within-group differences is often not reported and cannot be calculated because the within-group correlation is unknown. For meta-analysis of net-changes, one must thus estimate the SE based on an arbitrarily chosen correlation. The objective of this study is to better understand how to impute within-arm correlation for meta-analyses of continuous outcomes when data are missing, this study describes the range of correlation values in a representative set of studies with sufficient data reported, and simulates the effect of using different correlation values on meta-analysis summary estimates when imputing missing data. From articles available to us from previous systematic reviews and from trials registered at ClinicalTrials.gov, we selected those that prospectively compared two or more interventions for continuous outcomes and reported all three of: baseline means and SEs (or equivalent), final means and SEs, and within-group changes and SEs. From these data we back-calculated correlation values for each study group. We described these data and tested for patterns based on study characteristics. We assessed the bias on estimates of within-group change SEs by comparing reported SEs with imputed SEs using arbitrarily chosen correlation values. We simulated meta-analyses, assessing the bias, coverage, and accuracy of the summary estimates derived from studies with missing correlation data. We analyzed 811 within-group correlation values from 123 studies with 281 study groups. The median (interquartile range) within-group correlation values across all studies was 0.59 (0.40, 0.81). Active treatment groups had lower correlation values (median 0.54) than no treatment groups (median 0.73, P