OBJECTIVES: Cox regression and Kaplan-Meier estimates are often needed in post-approval clinical research which requires access to individual person data (IPD). Think about a long-term treatment-effect review or a comparative effectiveness oncology study involving the IPD owned by a competitor. Here, IPD cannot always be shared due to privacy or proprietary restrictions, which complicates the making of such estimates. I propose a method that generates pseudodata replacing the IPD by only using non-disclosive IPD aggregates which are shared with a central computer and are input parameters to a Gaussian copula (GC) generating the pseudodata, allowing analysts to access completely anonymised versions of possibly multiple datasets on which to run analyses.

METHODS: Survival inferences are computed on the pseudodata as if it was the IPD. I describe the utility of such pseudodata for different survival inferences via practical examples and simulations.

RESULTS: GC might have limitations in subgroup analyses but has similar utility to an IPD bootstrap distributed across centres which could offer a considerable improvement relative to standard approaches like summary-based meta-analysis or population adjusted network meta-analysis

CONCLUSIONS: GC avoids many legal problems related to IPD privacy or property while facilitating approximation of common IPD survival analyses otherwise difficult to conduct. This suggests that the standardised publication of certain IPD aggregates could greatly ease many “second purpose”-research activities often hindered by the lack of IPD.