Hierarchical Multivariate Directed Acyclic Graph Auto-Regressive (MDAGAR) models for spatial diseases mapping

02/04/2021
by   Leiwen Gao, et al.
0

Disease mapping is an important statistical tool used by epidemiologists to assess geographic variation in disease rates and identify lurking environmental risk factors from spatial patterns. Such maps rely upon spatial models for regionally aggregated data, where neighboring regions tend to exhibit similar outcomes than those farther apart. We contribute to the literature on multivariate disease mapping, which deals with measurements on multiple (two or more) diseases in each region. We aim to disentangle associations among the multiple diseases from spatial autocorrelation in each disease. We develop Multivariate Directed Acyclic Graphical Autoregression (MDAGAR) models to accommodate spatial and inter-disease dependence. The hierarchical construction imparts flexibility and richness, interpretability of spatial autocorrelation and inter-disease relationships, and computational ease, but depends upon the order in which the cancers are modeled. To obviate this, we demonstrate how Bayesian model selection and averaging across orders are easily achieved using bridge sampling. We compare our method with a competitor using simulation studies and present an application to multiple cancer mapping using data from the Surveillance, Epidemiology, and End Results (SEER) Program.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 8

page 11

page 19

04/30/2022

Bayesian Models for Multivariate Difference Boundary Detection in Areal Data

Regional aggregates of health outcomes over delineated administrative un...
11/26/2019

Spatial Modeling for Correlated Cancers Using Bivariate Directed Graphs

Disease maps are an important tool in cancer epidemiology used for the a...
08/14/2018

Discrete versus continuous domain models for disease mapping

Disease mapping aims to assess variation of disease risk over space and ...
09/17/2018

Spatial Variable Selection and An Application to Virginia Lyme Disease Emergence

Lyme disease is an infectious disease that is caused by a bacterium call...
04/05/2022

GeoSPM: Geostatistical parametric mapping for medicine

The characteristics and determinants of health and disease are often org...
09/21/2021

A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects

In disease mapping, the relative risk of a disease is commonly estimated...
12/18/2019

Multidimensional molecular changes-environment interaction analysis for disease outcomes

For the outcomes and phenotypes of complex diseases, multiple types of m...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.