MODEL SELECTION FOR MATERNAL HYPERTENSIVE DISORDERS WITH SYMMETRIC HIERARCHICAL DIRICHLET PROCESSES

• 成果类型: Article
• 署名作者: Ranzolini, Beatricef; Ljoi, Antoniol; Ruenster, Igorp
• 署名单位: Agency for Science Technology & Research (A*STAR); A*STAR - Singapore Institute for Clinical Sciences (SICS); Bocconi University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1628
• 发表日期: 2023
• 页码: 313-332
• 关键词: bayesian-inference; cardiac-function; risk-factors; preeclampsia; disease

摘要

Hypertensive disorders of pregnancy occur in about 10% of pregnant women around the world. Though there is evidence that hypertension im-pacts maternal cardiac functions, the relation between hypertension and car-diac dysfunctions is only partially understood. The study of this relationship can be framed as a joint inferential problem on multiple populations, each corresponding to a different hypertensive disorder diagnosis, that combines multivariate information provided by a collection of cardiac function indexes. A Bayesian nonparametric approach seems particularly suited for this setup, and we demonstrate it on a dataset consisting of transthoracic echocardiog-raphy results of a cohort of Indian pregnant women. We are able to perform model selection, provide density estimates of cardiac function indexes and a latent clustering of patients: these readily interpretable inferential outputs allow to single out modified cardiac functions in hypertensive patients, com-pared to healthy subjects, and progressively increased alterations with the severity of the disorder. The analysis is based on a Bayesian nonparametric model that relies on a novel hierarchical structure, called symmetric hierar-chical Dirichlet process. This is suitably designed so that the mean parameters are identified and used for model selection across populations, a penalization for multiplicity is enforced, and the presence of unobserved relevant factors is investigated through a latent clustering of subjects. Posterior inference relies on a suitable Markov chain Monte Carlo algorithm, and the model behaviour is also showcased on simulated data.