Scientific knowledge accumulates and therefore always has a (partly) sequential nature. As a result, the exchangeability assumption in conventional meta-analysis cannot be met if the existence of a replication — or generally: later studies in a series — depends on earlier results. Such dependencies arise at the study level but also at the meta-analysis level, if new studies are informed by a systematic review of existing results in order to reduce research waste. Fortunately, studies series with such dependencies can be meta-analyzed with Safe Tests. These tests preserve type I error control, even if the analysis is updated after each new study. Moreover, they introduce a novel approach to handling heterogeneity; a bottleneck in sequential meta-analysis. This strength of Safe Tests for composite null hypotheses lies in controlling type I errors over the entire set of null distributions by specifying the test statistic for a worst-case prior on the null. If for each study such a (study-specific) test statistic is provided, the combined test controls type I error even if each study is generated by a different null distribution. These properties are optimized in so-called GROW Safe Tests. Hence, they optimize the ability to reject the null hypothesis and make intermediate decisions in a growing series, without the need to model heterogeneity.