Distributed Multivariate Regression Modeling For Selecting Biomarkers Under Data Protection Constraints

TL;DR

This paper introduces a distributed multivariate regression method for biomarker selection that operates under strict data protection constraints, enabling joint analysis without sharing individual-level data.

Contribution

It presents a novel iterative variable selection approach using only aggregated data, with a heuristic variant reducing data calls, implemented within the DataSHIELD framework.

Findings

Method achieves results equivalent to pooled data analysis with local standardization.

Heuristic reduces data calls from over 10 to 3 in typical scenarios.

Minimal information loss with local standardization in simulations.

Abstract

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.\