Speaker
Description
In many biomedical research settings, sufficiently large sample sizes can only be achieved by combining data from multiple collection sites (e.g., hospitals). However, pooling individual participant data in a central server is often restricted due to privacy and regulatory constraints. Federated inference addresses this challenge by distributing the statistical analysis across local sites, allowing pooled inference in a central server using privacy-preserving summary statistics. Although federated inference methods exist in a frequentist framework, the full potential of Bayesian approaches in this area has not yet been explored. Bayesian methods offer distinct advantages, including the ability to incorporate prior knowledge and to perform predictive checks for model criticism. A recently published Bayesian method for federated inference relies on approximate solutions even in linear regression scenarios where exact solutions are available. We therefore propose a different approach to federated inference using Bayesian conjugate analysis (BCA), which is communication-efficient and mathematically convenient. Moreover, BCA yields exact, lossless inference for linear regression problems, that is, producing the same posterior distribution as if the pooled data had been analyzed. We further show that BCA can also be used as an approximation for more general inference problems where the parameters to be estimated are asymptotically normal, such as generalized linear models. Finally, the BCA approach naturally lends itself to Reverse-Bayes analysis, which can be used for computationally efficient predictive checks and identification of outlier sites. An implementation of BCA is freely available through the confeR package (conjugate federated analysis in R).
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