Assessing Area under the Curve as an Alternative to Latent Growth Curve Modeling for Repeated Measures Zero-Inflated Poisson Data: A Simulation Study

• Published in: Stats (Basel, Switzerland) Vol. 6; no. 1; pp. 354 - 364
• Main Author: Rodriguez, Daniel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: area under the curve; Data analysis; Drug use; Generalized linear models; Growth curve modeling; latent growth curve modeling; longitudinal data; Monte Carlo method; Monte Carlo simulation; Monte Carlo simulation study; Poisson distribution; Random variables; Software; Substance abuse; substance use; Vaping; Within-subjects design; zero-inflated Poisson distribution; United States
• ISSN: 2571-905X; 2571-905X
• DOI: 10.3390/stats6010022

Researchers interested in the assessment of substance use trajectories, and predictors of change, have several data analysis options. These include, among others, generalized estimating equations and latent growth curve modeling. One difficulty in the assessment of substance use, however, is the nature of the variables studied. Although counting instances of use (e.g., the number of cigarettes smoked per day) would seem to be the best option, such data present difficulties in that the distribution of these variables is not likely normal. Count variables often follow a Poisson distribution, and when dealing with substance use in the general population, there is a preponderance of zeros (representing not using). As such, substance use counts may approximate a zero-inflated Poisson distribution. Unfortunately, analyses with zero-inflated Poisson random variables are not easily accommodated in many types of software and may be beyond access to most researchers. As such, an easier method would benefit researchers interested in assessing substance use change. The purpose of this study is to assess the area under the curve as an option when dealing with repeated measures data and contrast it to one popular method of longitudinal data analysis, latent growth curve modeling. Using a Monte Carlo simulation study with varying sample sizes, we found that the area under the curve performed well with different sample sizes and compared favorably to the performance of latent growth curve modeling, particularly when dealing with smaller sample sizes. The area under the curve may be a simpler alternative for researchers, especially when dealing with smaller sample sizes.