Bayesian multi-study regression factor analysis

De Vito Roberta, Brown University

Adopting methods to integrate multiple studies is crucial to achieving knowledge and information in epidemiological and biological data. This integration relies on two key challenges: 1- the common amount of information from all the studies and 2- the study-specific source from individual studies. The Bayesian Multi-study Factor model (De Vito et al., 2019) achieves these two challenges and handles multiple studies. We propose a novel sparse Bayesian Multi-study Factor model by adopting a latent factor regression approach (Avalos-Pacheco et al., 2021). This generalization recovers the study and the common component, keeping track of the observed variables, such as demographic information. We consider spike and slab sparse priors (local and non-local) to detect the latent dimension, enhancing the factor cardinality reconstruction. A user-defined prior dispersion for the regression coefficient accounts for population structure and other subject characteristics. We derive a computationally fast dynamic ECM. We assess the characteristics of our method using different simulation settings. We clarify the benefit of our model, resulting in better accuracy and precision. We illustrate the advantages of our method through a nutritional epidemiological application. We identify study-specific and shared signals across studies accounting for covariates, such as smoking, alcohol, and age.

Area: CS9 - Bayesian Inference in Stochastic Processes (Alejandra Avalos-Pacheco)

Keywords: multi-study, factor analysis, covariates

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