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Statistical modeling of Agatston score in multi-ethnic study of atherosclerosis (MESA)
Shuangge Ma1, Anna Liu, Jeffrey Carr
1School of Public Health, Yale University, New Haven, Connecticut, United States of America. Shuangge.ma@yale.edu
Insights
Statistical models for coronary artery calcium (CAC) in the Multi-Ethnic Study of Atherosclerosis (MESA) were investigated. Semiparametric models best describe CAC development, while parametric models suffice for prediction.
Area of Science:
- Cardiovascular Disease Epidemiology
- Biostatistics
- Medical Imaging Analysis
Background:
- The Multi-Ethnic Study of Atherosclerosis (MESA) cohort provides data on subclinical cardiovascular disease.
- Coronary artery calcium (CAC) is a key risk factor for coronary heart disease, with a zero-inflated distribution in MESA.
- Existing statistical models for zero-inflated data have limitations, including reliance on parametric assumptions and limited focus on estimation and prediction properties.
Purpose of the Study:
- To investigate advanced statistical modeling techniques for analyzing coronary artery calcium (CAC) data from the MESA study.
- To compare the estimation and prediction properties of various two-part statistical models, including parametric and semiparametric, proportional and nonproportional variants.
- To determine the most appropriate statistical approach for fully describing the relationship between covariates and CAC development versus predicting CAC.
Main Methods:
- Utilized two-part statistical models to analyze the zero-inflated distribution of coronary artery calcium (CAC) scores in the MESA cohort.
- Investigated both parametric and semiparametric modeling approaches.
- Examined both proportional and nonproportional covariate effect models to assess their estimation and prediction capabilities.
Main Results:
- Semiparametric models with nonproportional covariate effects are necessary for a comprehensive description of the relationship between covariates and CAC development.
- Parametric models with proportional covariate effects are adequate and sufficient for the prediction of CAC.
- The study highlights distinct modeling requirements for understanding CAC etiology versus predicting future risk.
Conclusions:
- The choice of statistical model is crucial and depends on the research objective: understanding mechanisms versus prediction.
- Semiparametric models offer deeper insights into the biological mechanisms driving CAC development.
- Parametric models provide a practical and efficient tool for predicting CAC scores in clinical and research settings.
Abstract:
The MESA (Multi-Ethnic Study of Atherosclerosis) is an ongoing study of the prevalence, risk factors, and progression of subclinical cardiovascular disease in a multi-ethnic cohort. It provides a valuable opportunity to examine the development and progression of CAC (coronary artery calcium), which is an important risk factor for the development of coronary heart disease. In MESA, about half of the CAC scores are zero and the rest are continuously distributed. Such data has been referred to as "zero-inflated data" and may be described using two-part models. Existing two-part model studies have limitations in that they usually consider parametric models only, make the assumption of known forms of the covariate effects, and focus only on the estimation property of the models. In this article, we investigate statistical modeling of CAC in MESA. Building on existing studies, we focus on two-part models. We investigate both parametric and semiparametric, and both proportional and nonproportional models. For various models, we study their estimation as well as prediction properties. We show that, to fully describe the relationship between covariates and CAC development, the semiparametric model with nonproportional covariate effects is needed. In contrast, for the purpose of prediction, the parametric model with proportional covariate effects is sufficient. This study provides a statistical basis for describing the behaviors of CAC and insights into its biological mechanisms.
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