Semiparametric zero-inflated modeling in multi-ethnic study of atherosclerosis (MESA)

TL;DR

This paper introduces a flexible semiparametric zero-inflated modeling approach for analyzing coronary artery calcium scores in a multi-ethnic cohort, revealing new insights into biological mechanisms of CAC development.

Contribution

It develops a novel model selection procedure and applies cubic regression splines for simultaneous variable selection and estimation in zero-inflated data.

Findings

Unconstrained zero-inflated normal model fits CAC data better.

Identifies distinct biological processes for CAC initiation and progression.

Results differ significantly from previous studies, offering new biological insights.

Abstract

We analyze the Agatston score of coronary artery calcium (CAC) from the Multi-Ethnic Study of Atherosclerosis (MESA) using the semiparametric zero-inflated modeling approach, where the observed CAC scores from this cohort consist of high frequency of zeroes and continuously distributed positive values. Both partially constrained and unconstrained models are considered to investigate the underlying biological processes of CAC development from zero to positive, and from small amount to large amount. Different from existing studies, a model selection procedure based on likelihood cross-validation is adopted to identify the optimal model, which is justified by comparative Monte Carlo studies. A shrinkaged version of cubic regression spline is used for model estimation and variable selection simultaneously. When applying the proposed methods to the MESA data analysis, we show that the two biological mechanisms influencing the initiation of CAC and the magnitude of CAC when it is positive are better characterized by an unconstrained zero-inflated normal model. Our results are significantly different from those in published studies, and may provide further insights into the biological mechanisms underlying CAC development in humans. This highly flexible statistical framework can be applied to zero-inflated data analyses in other areas.