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The discovery of clinical biomarkers requires large patient cohorts and isaided by a pooled data approach across institutions. In many countries, dataprotection constraints, especially in the clinical environment, forbid theexchange of individual-level data between different research institutes,impeding the conduct of a joint analyses. To circumvent this problem, onlynon-disclosive aggregated data is exchanged, which is often done manually andrequires explicit permission before transfer, i.e., the number of data callsand the amount of data should be limited. This does not allow for more complextasks such as variable selection, as only simple aggregated summary statisticsare typically transferred. Other methods have been proposed that require morecomplex aggregated data or use input data perturbation, but these methods caneither not deal with a high number of biomarkers or lose information. Here, wepropose a multivariable regression approach for identifying biomarkers byautomatic variable selection based on aggregated data in iterative calls, whichcan be implemented under data protection constraints. The approach can be usedto jointly analyze data distributed across several locations. To minimize theamount of transferred data and the number of calls, we also provide a heuristicvariant of the approach. When performing global data standardization, theproposed method yields the same results as pooled individual-level dataanalysis. In a simulation study, the information loss introduced by localstandardization is seen to be minimal. In a typical scenario, the heuristicdecreases the number of data calls from more than 10 to 3, rendering manualdata releases feasible. To make our approach widely available for application,we provide an implementation of the heuristic version incorporated in theDataSHIELD framework.\

Distributed Multivariate Regression Modeling For Selecting Biomarkers Under Data Protection Constraints