Check the preview of 2nd version of this platform being developed by the open MLCommons taskforce on automation and reproducibility as a free, open-source and technology-agnostic on-prem platform.

Distributed regression modeling for selecting markers under data protection constraints

lib:7cbee7a9c977630c (v1.0.0)

Vote to reproduce this paper and share portable workflows   1 
Authors: Daniela Zöller,Stefan Lenz,Harald Binder
ArXiv: 1803.00422
Document:  PDF  DOI 
Artifact development version: GitHub
Abstract URL:

Data protection constraints frequently require a distributed analysis of data, i.e., individual-level data remains at many different sites, but analysis nevertheless has to be performed jointly. The corresponding aggregated data is often exchanged manually, requiring explicit permission before transfer, i.e., the number of data calls and the amount of data should be limited. Thus, only simple aggregated summary statistics are typically transferred with just a single call. This does not allow for more complex tasks such as variable selection. As an alternative, we propose a multivariable regression approach for identifying important markers by automatic variable selection based on aggregated data from different locations in iterative calls. To minimize the amount of transferred data and the number of calls, we also provide a heuristic variant of the approach. When performing a global data standardization, the proposed methods yields the same results as when pooling individual-level data. In a simulation study, the information loss introduced by a 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 on top of the DataSHIELD framework.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives


Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!