对重复测量零膨胀泊松数据的曲线下面积评估作为潜在增长曲线模型的替代方法:模拟研究

Pub Date : 2023-02-19 DOI:10.3390/stats6010022
Daniel Rodriguez
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引用次数: 2

摘要

对物质使用轨迹评估和变化预测感兴趣的研究人员有几种数据分析选择。其中包括广义估计方程和潜在增长曲线模型。然而,评估药物使用的一个困难是所研究变量的性质。虽然计算使用实例(例如,每天吸烟的数量)似乎是最好的选择,但这些数据存在困难,因为这些变量的分布不太可能是正态的。计数变量通常遵循泊松分布,当处理一般人群中的物质使用时,存在零的优势(表示不使用)。因此,物质使用计数可能近似于零膨胀的泊松分布。不幸的是,使用零膨胀泊松随机变量的分析在许多类型的软件中不容易容纳,并且可能超出大多数研究人员的访问范围。因此,一种更简单的方法将有利于对评估物质使用变化感兴趣的研究人员。本研究的目的是评估曲线下的面积,作为处理重复测量数据时的一种选择,并将其与一种流行的纵向数据分析方法——潜在增长曲线模型进行对比。通过对不同样本量的蒙特卡罗模拟研究,我们发现曲线下的面积在不同样本量下表现良好,并且与潜在增长曲线建模的性能相比,特别是在处理较小样本量时。对于研究人员来说,曲线下面积可能是一个更简单的选择,特别是在处理较小的样本量时。
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Assessing Area under the Curve as an Alternative to Latent Growth Curve Modeling for Repeated Measures Zero-Inflated Poisson Data: A Simulation Study
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.
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