Cumulative meta-analysis (CMA) aims to aggregate accumulating evidence. Essentially a visual tool, CMA should be supplemented by formal statistical methods for assessment of the significance of the accumulating evidence, and for detection of temporal trends in effect sizes. These methods should also take into account multiple testing inherent in CMA. We review the existing methods for detection of temporal trends in effect sizes and suggest a new approach, namely the use of standard quality control (QC) charts, in particular X charts and CUSUM charts, to detect possible outliers and trends over time. We discuss the application of the QC charts to four popular measures of effect size: the odds ratios, the relative risks, the correlation coefficients and the standardized mean differences. Applications of QC charts are illustrated by three meta-analysis examples from medicine, ecology and evolutionary biology. Copyright © 2011 John Wiley & Sons, Ltd.