Meta-analysis is important in oncological research to provide a more reliable answer to a clinical research question that was assessed in multiple studies but with inconsistent results. Pair-wise meta-analysis can be applied when comparing two treatments at once, whereas it is possible to compare multiple treatments at once with network meta-analysis (NMA). After careful systematic review of the literature and quality assessment of the identified studies, there are several assumptions in the use of meta-analysis. First, the added value of meta-analysis should be evaluated by examining the comparability of study populations. Second, the appropriate comparator in meta-analysis should be chosen according to the types of comparisons made in individual studies: (1) Experimental and comparator arms are different treatments (A vs. B); (2) Substitution of a conventional treatment by an experimental treatment (A+B vs. A+C); or (3) Addition of an experimental treatment (A+B vs. B). Ideally there is one common comparator treatment, but when there are multiple common comparators, the most efficacious comparator is preferable. Third, treatments can only be adequately pooled in meta-analysis or merged into one treatment node in NMA when considering likewise mechanism of action and similar setting in which treatment is indicated. Fourth, for both pair-wise meta-analysis and NMA, adequate assessment of heterogeneity should be performed and sub-analysis and sensitivity analysis can be applied to objectify a possible confounding factor. Network inconsistency, as statistical manifestation of violating the transitivity assumption, can best be evaluated by node-split modeling. NMA has advantages over pair-wise meta-analysis, such as clarification of inconsistent outcomes from multiple studies including multiple common comparators and indirect effect calculation of missing direct comparisons between important treatments. Also, NMA can provide increased statistical power and cross-validation of the observed treatment effect of weak connections with reasonable network connectivity and sufficient sample-sizes. However, inappropriate use of NMA can cause misleading results, and may emerge when there is low network connectivity, and therefore low statistical power. Furthermore, indirect evidence is still observational and should be interpreted with caution. NMA should therefore preferably be conducted and interpreted by both expert clinicians in the field and an experienced statistician. Finally, the use of meta-analysis can be extended to other areas, for example the identification of prognostic and predictive factors. Also, the integration of evidence from both meta-analysis and expert opinion can improve the construction of prognostic models in real-world databases.