During drug development, a biomarker is sometimes identified as separating a patient population into those with more and those with less benefit from evaluated treatments. Consequently, later studies might be targeted, while earlier ones are performed in mixed patient populations. This poses a challenge in evidence synthesis, especially if only aggregated data are available. Starting from this scenario, we investigate three commonly used network meta-analytic estimation methods, the naive estimation approach, the stand-alone analysis, and the network meta-regression. Additionally, we adapt and modify two methods, which are used in evidence synthesis to combine randomized controlled trials with observational studies, the enrichment-through-weighting approach, and the informative prior estimation. We evaluate all five methods in a simulation study with 32 scenarios using bias, root-mean-squared-error, coverage, precision, and power. Additionally, we revisit a clinical data set to exemplify and discuss the application. In the simulation study, none of the methods was observed to be clearly favorable over all investigated scenarios. However, the stand-alone analysis and the naive estimation performed comparably or worse than the other methods in all evaluated performance measures and simulation scenarios and are therefore not recommended. While substantial between-trial heterogeneity is challenging for all estimation approaches, the performance of the network meta-regression, the enriching-through weighting approach and the informative prior approach was dependent on the simulation scenario and the performance measure of interest. Furthermore, as these estimation methods are drawing slightly different assumptions, some of which require the presence of additional information for estimation, we recommend sensitivity-analyses wherever possible.