Probabilistic record linkage (RL) is increasingly used to combine multiple data sources at the level of individual records. While the resulting linked data sets carry significant potential for scientific discovery and informing decisions, data contamination arising from mismatched records as a result of non-unique or noisy identifiers used for linking is not uncommon. Accounting for such mismatch errors in the downstream analysis performed on the linked file is critical to ensure valid statistical inference. In this chapter, we present an approach to enable valid post-linkage inference in the analysis of survival data when survival times reside in one file and covariates in another file. The proposed approach addresses the secondary analysis setting in which only the linked file (but not the two individual files) is given and is built on a general framework based on a mixture model (Slawski et al., in A general framework for regression with mismatched data based on mixture modeling [29]). According to that framework, inference can be conducted in terms of composite likelihood and the EM algorithm, enhanced by careful modifications to address the semiparametric nature of the Cox PH model. The effectiveness of the approach is investigated by simulation studies and an illustrative case study.