MCAR Modeling with Controlled Informativeness to Avoid Oversmoothing

Music City Center 

In disease mapping, multivariate CAR models are commonly used to account for dependencies between multiple diseases that share risk factors. One can also jointly model different demographic groups for a single disease through using an MCAR model to borrow strength across related populations. Prior studies have raised concerns about the univariate CAR model for its tendency to produce estimates that are overly smooth and overly precise compared to the amount of information contained in the data. Multivariate models are inherently more informative, as they draw from more sources of information, yet no method has been proposed to quantify the effect of this. Our study addresses this gap by presenting a method to measure the informativeness of the MCAR model compared to the CAR model and applying the framework to a dataset comprised of county-level heart disease death counts stratified by race/ethnicity and sex. After demonstrating the degree to which the MCAR model can lead to oversmoothing, we illustrate how to restrict the model's informativeness to ensure that the precision of our estimates is consistent across groups and commensurate with the amount of data observed.

Spatial Statistics

Disease Mapping

Small Area Estimation 

Section on Statistics in Epidemiology