Deploying survival models in clinical settings requires both interpretability and transferability (that is, models that are easy to deploy) [Klau et al., 2018, Boulesteix et al., 2017]. The gold standard are linear models trained on only clinical data and at most one molecular data group, such as gene expression. However, black-box methods for multi-omics integration such as BlockForest [Hornung and Wright, 2019] have recently been shown to outperform both the clinical Cox Proportional Hazards model and multi-omics adapted linear models in terms of concordance [Herrmann et al., 2021, Hornung and Wright, 2019]. Thus, there is a need to make multi-omics methods amenable to clinical settings to leverage their excellent performance. We propose to use surrogate models, a technique long used in interpretable machine learning [Molnar, 2020], to create sparse linear models as surrogates for black-box multi-omics models. We show that these surrogates yield better performance than linear models trained directly on the input datasets and still achieve relatively high sparsity levels. Our implementation is available on Github (Link is embedded when clicking on "Github" - note that the repo may give an indication as to the authors’ affiliation).