An example of a two-part latent growth curve model for semicontinuous outcomes in the health sciences

Abstract

A new method of modeling coronary artery calcium (CAC) is needed in order to properly understand the probability of onset and growth of CAC. CAC remains a controversial indicator of cardiovascular disease (CVD) risk, but this may be due to ill-equipped methods of specifying CAC during the analysis phase of studies reporting an analysis where CAC is the primary outcome. The modern method of two-part latent growth modeling may represent a strong alternative to the myriad of existing methods for modeling CAC. We provide a brief overview of existing methods of analysis used for CAC before introducing the general latent growth curve model, how it extends into a two-part (semicontinuous) growth model, and how the ubiquitous problem of missing data can be effectively handled. We then present an example of how to model CAC using this framework. We demonstrate that utilizing this type of modeling strategy can result in traditional predictors of CAC (e.g. age, gender, and high-density lipoprotein cholesterol), exerting a different impact on the two different, yet simultaneous, operationalizations of CAC. This method of analyzing CAC could inform future analyses of CAC and inform subsequent discussions about the nature of its potential to inform long-term CVD risk and heart events.

Keywords:

• two-part growth modeling
• structural equation modeling
• latent variable models
• coronary artery calcium
• cardiovascular disease

Acknowledgements

We thank the entire SHS team, in addition to Craig Parks and Leonard Burns, for their helpful comments throughout the development of this project. Without the hard work of the SHS founder, Harold Mielke, this work would not have been possible. We also thank two anonymous reviewers who provided detailed and thoughtful comments that helped make this manuscript significantly stronger.