In most of meta-analyses of aggregated survival data, a pooled measure of the intervention's effect is obtained by combining reported hazard ratios. Advanced statistical methods have been proposed for a better characterization of the effect. With these methods, published survival curves can be synthesized in a single summary survival curve and a variation over follow-up time of the effect can be tested. Besides the pooled intervention's effect, the synthesis of survival in each arm is helpful for the appraisal of the intervention's benefits. Moreover, some meta-analyses aim to evaluate survival in a single group. In meta-analysis of comparative studies, testing a variation of the effect over time can avoid simplistic conclusions. These methods have been published in journals of statistics and may not be accessible to a large audience. The purpose of this article is to review novel methods for meta-analysis of aggregated survival data. The principle of each method, and its advantages and limitations are explained.