Abstract
Psychiatric studies of suicide provide fundamental insights on the evolution of severe psychopathologies, and contribute to the development of early treatment interventions. Our focus is on modelling different traits of psychosis and their interconnections, focusing on a case study on suicide attempt survivors. Such aspects are recorded via multivariate categorical data, involving a large numbers of items for multiple subjects. Current methods for multivariate categorical data—such as penalized log-linear models and latent structure analysis—are either limited to low-dimensional settings or include parameters with difficult interpretation. Motivated by this application, this article proposes a new class of approaches, which we refer to as Mixture of Log Linear models (mills). Combining latent class analysis and log-linear models, mills defines a novel Bayesian approach to model complex multivariate categorical data with flexibility and interpretability, providing interesting insights on the relationship between psychotic diseases and psychological aspects in suicide attempt survivors.
Funding Statement
This work was partially funded by MIUR–PRIN 2017 project 20177BR-JXS, as well as grant R01ES027498 of the National Institute of Environmental Health Sciences of the United States Institutes of Health, and grant N00014-16-1-2147 from the United States Office of Naval Research.
Acknowledgments
The case study illustrated in this work has been motivated by a collaboration with doctor Paolo Scocco from Padova Hospital, which is kindly acknowledged for providing the data and the stimulating discussions. Emanuele Aliverti would also like to acknowledge Prof. Giovanna Capizzi, Massimiliano Russo and Daniele Durante for the inspiring discussions on the first draft of this work. A preliminary version of this work was included in the Ph.D. dissertation of Emanuele Aliverti.
Citation
Emanuele Aliverti. David B. Dunson. "Composite mixture of log-linear models with application to psychiatric studies." Ann. Appl. Stat. 16 (2) 765 - 790, June 2022. https://doi.org/10.1214/21-AOAS1515
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