In science, the gold standard for evidence is an empirical result which is consistent across multiple studies. Meta-analysis techniques allow researchers to combine the results of different studies. Due to the increasing availability of longitudinal data, studying lagged effects is increasingly popular also in meta-analytic studies. However, in current practice, little attention is paid to the unique challenges of meta-analyzing these lagged effects. Namely, it is well known that lagged effects estimates change depending on the time that elapses between measurement waves. This means that studies that use different uniform time intervals between observations (e.g., 1 hour vs 3 hours or 1 month vs 2 months) can come to very different parameter estimates, and seemingly contradictory conclusions, about the same underlying process. In this article, we introduce, describe, and illustrate a new meta-analysis method (CTmeta) which assumes an underlying continuous-time process, and compare it with current practice.