dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD.
Sofack, Ghislain N
Bishop, Tom RP
BMC Res Notes
Springer Science and Business Media LLC
MetadataShow full item record
Banerjee, S., Sofack, G. N., Papakonstantinou, T., Avraam, D., Burton, P., Zöller, D., & Bishop, T. R. (2022). dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD.. [Other]. https://doi.org/10.1186/s13104-022-06085-1
OBJECTIVE: Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but an analytic workflow involving local analysis undertaken at individual studies hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. RESULTS: We introduce a package (dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data.
Research Note, Survival analysis, Meta-analysis, Federated analysis
European Commission Horizon 2020 (H2020) Societal Challenges (824989)
External DOI: https://doi.org/10.1186/s13104-022-06085-1
This record's DOI: https://doi.org/10.17863/CAM.85192