Network meta-analysis (NMA) technique extends the standard meta-analysis methods, allowing pairwise comparison of all treatments in a network in the absence of head-to-head comparisons. Traditional NMA models consider a single endpoint for each trial. However, in many cases, trials in the network have different durations and/or report data at multiple time points. Moreover, these time points are often not the same for all trials. In this work, we review the most relevant methods that incorporate multiple time points and allow indirect comparisons of treatment effects across different longitudinal studies. In particular, we focus on the mixed treatment comparison developed by Dakin et al, on the Bayesian evidence synthesis techniques—integrated two-component prediction developed by Ding et al, and on the more recent method based on fractional polynomials by Jansen et al. We highlight the main features of each model and illustrate them in simulations and in a real data application. Our study shows that methods based on fractional polynomials offer a flexible modeling strategy in most applications.