Multi-site health research integrating complementary data sources: A scoping review of statistical inference methods...

To address the multidimensional nature of health-related questions, advances in health research often require integrating information from various data sources within statistical analyses. When complementary information pertaining to the same set of individuals are distributed across different institutions, vertical methods make it possible to obtain analysis results without sharing or pooling individual-level data. To guide stakeholders toward a transparent and rigorous use of vertical methods with sensitive health data, this study aims to (1) Identify existing vertical methods enabling statistical inference (confidence interval estimation and hypothesis testing); and (2) Characterize the methodological properties of these methods and the current extent of their use with health data.

We conducted a scoping review following PRISMA-ScR using four interdisciplinary databases. We then systematically extracted the characteristics of identified vertical methods with respect to comparability with the pooled analysis, efficiency of communication schemes and confidentiality. We additionally screened studies that cited included articles to identify applications on vertically partitioned real-world health data.

Among 2887 articles initially screened, 30 were included in the review, of which a majority mentioned health analytics. Inference for the linear and the logistic regression framework were the most frequent statistical inference tasks undertaken in proposed methods. Equivalence with the pooled analyses was not systematically addressed and most methods required multiple communications between participating parties. Almost all articles described their approach as privacy-preserving, although a minority provided privacy assessments. Very few published health studies were found to report the use these methods.

The scope of existing approaches enabling statistical inference for vertically partitioned data is still relatively limited. Most existing methods do not concurrently achieve results equivalent to centralized analyses, high communication efficiency, and guaranteed protection of individual-level data.