  The discovery of clinical biomarkers requires large patient cohorts and is
aided by a pooled data approach across institutions. In many countries, data
protection constraints, especially in the clinical environment, forbid the
exchange of individual-level data between different research institutes,
impeding the conduct of a joint analyses. To circumvent this problem, only
non-disclosive aggregated data is exchanged, which is often done manually and
requires explicit permission before transfer, i.e., the number of data calls
and the amount of data should be limited. This does not allow for more complex
tasks such as variable selection, as only simple aggregated summary statistics
are typically transferred. Other methods have been proposed that require more
complex aggregated data or use input data perturbation, but these methods can
either not deal with a high number of biomarkers or lose information. Here, we
propose a multivariable regression approach for identifying biomarkers by
automatic variable selection based on aggregated data in iterative calls, which
can be implemented under data protection constraints. The approach can be used
to jointly analyze data distributed across several locations. To minimize the
amount of transferred data and the number of calls, we also provide a heuristic
variant of the approach. When performing global data standardization, the
proposed method yields the same results as pooled individual-level data
analysis. In a simulation study, the information loss introduced by local
standardization is seen to be minimal. In a typical scenario, the heuristic
decreases the number of data calls from more than 10 to 3, rendering manual
data releases feasible. To make our approach widely available for application,
we provide an implementation of the heuristic version incorporated in the
DataSHIELD framework.\
