Distributed regression modeling for selecting markers under data protection constraints

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.

Modeling Activity Tracker Data Using Deep Boltzmann Machines

Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mostly unlabeled data pose a challenge to statistical modeling approaches. To investigate the feasibility of deep learning approaches for unsupervised learning with such data, we examine weekly usage patterns of Fitbit activity trackers with deep Boltzmann machines (DBMs). This method is particularly suitable for modeling complex joint distributions via latent variables. We also chose this specific procedure because it is a generative approach, i.e., artificial samples can be generated to explore the learned structure. We describe how the data can be preprocessed to be compatible with binary DBMs. The results reveal two distinct usage patterns in which one group frequently uses trackers on Mondays and Tuesdays, whereas the other uses trackers during the entire week. This exemplary result shows that DBMs are feasible and can be useful for modeling activity tracker data.