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A novel application of Bayesian methods for modeling substance use trajectories

贝叶斯方法在物质使用轨迹建模中的新颖应用

基本信息

批准号：
8884571
负责人：
Sierra Bainter
金额：
$ 2.88万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-01 至 2016-05-15
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Substance use is one of the most commonly occurring health risk behaviors in adolescence and has been unambiguously linked to a variety of negative physical, biological, and psychological outcomes (e.g., Hingson & Kenkel, 2004; USDHHS, 2007). These serious public health issues have impelled substantial growth in the theoretical conceptualization of pathways to substance use, especially factors that exist in childhood and adolescence (e.g., Hussong et al., 2011; Zucker, Heitzeg, & Nigg, 2011). However, researchers currently disagree as to whether patterns of onset and escalation are best captured through the identification of discrete groups or "types" of individuals or through modeling individual variability across continuous trajectories of use. Growth mixture models are widely used to identify qualitatively different subgroups, yet key limitations of these models directly undermine the extent to which competing theories of substance use and abuse can be validly tested and compared. It has been extensively demonstrated that maximum likelihood (ML), the current gold-standard method for estimating growth mixture models, is unable to reliably reproduce the true population structure. Mixture models are highly sensitive to even slight model misspecifications or improper restrictions, and if data are non-normally distributed spurious classes will be detected (Bauer & Curran, 2003a; 2004). If latent classes truly exist, it is difficult to correctly determine the number and form of latent trajectory classes using existing fit statistics (Tofighi & Enders, 2008). Compared to ML, there is the potential for more parameters and more complex models to be identified in a Bayesian analysis (Muthen & Asparouhov, in press), and Bayesian estimation is more stable and has more power in small samples (Asparouhov & Muthen, 2010). If a model with too many classes is estimated (Rousseau & Mengersen, 2011), Bayesian estimation will reliably "empty" the unneeded classes whereas ML estimation becomes unstable. In a fully Bayesian latent class analysis, the distribution of the number of classes can be estimated and examined as an unknown parameter (Richardson & Greene, 1997), whereas no information of this kind is available using ML estimation. However, Bayesian estimation still needs to be rigorously studied in the context of adolescent substance use. The three core aims of my project are to (1) compare Bayesian and ML estimation of growth mixture models when the population is truly homogenous; (2) compare Bayesian and ML estimation when multiple trajectory classes do truly exist; and (3) apply novel Bayesian methods to existing adolescent substance use data. My proposed project will fully integrate advanced quantitative methods with substantive theory so that researchers can reliably and validly test developmental theories of adolescent substance use.

描述（由申请人提供）：物质使用是青春期最常见的健康风险行为之一，并已明确与各种负面的身体，生物和心理结果（例如，Hingson & Kenkel，2004年; USDHHS，2007年）。这些严重的公共卫生问题推动了物质使用途径的理论概念化的实质性发展，特别是儿童和青少年时期存在的因素（例如，Hussong等人，2011; Zucker，Heitzeg，& Nigg，2011）.然而，研究人员目前不同意是否发作和升级的模式最好通过识别离散群体或“类型”的个人或通过建模的个人变异性在连续的使用轨迹。增长混合模型被广泛用于确定质量不同的亚组，但这些模型的主要局限性直接破坏了竞争的药物使用和滥用理论可以有效地测试和比较的程度。它已被广泛证明，最大似然（ML），目前的黄金标准方法估计增长的混合模型，是无法可靠地再现真实的人口结构。混合模型对即使是轻微的模型错误或不适当的限制都非常敏感，如果数据是非正态分布的，则会检测到虚假的类别（Bauer & Curran，2003 a; 2004）。如果潜在类确实存在，则难以使用现有方法正确地确定潜在轨迹类的数量和形式。拟合统计（Toxii & Enders，2008）。与ML相比，在贝叶斯分析中有可能识别更多的参数和更复杂的模型（Muthen & Asparouhov，出版中），贝叶斯估计更稳定，在小样本中具有更大的功效（Asparouhov & Muthen，2010）。如果一个模型有太多的类被估计（Rousseau & Mengersen，2011），贝叶斯估计将可靠地“清空”不需要的类，而ML估计变得不稳定。在完全贝叶斯潜在类分析中，类数的分布可以作为未知参数进行估计和检查（Richardson &格林，1997），而使用ML估计则无法获得此类信息。然而，贝叶斯估计仍然需要在青少年物质使用的背景下进行严格的研究。我的项目的三个核心目标是（1）当人口真正同质时，比较贝叶斯和ML估计的增长混合模型;（2）当多个轨迹类确实存在时，比较贝叶斯和ML估计;（3）将新的贝叶斯方法应用于现有的青少年物质使用数据。我所提出的项目将充分结合先进的定量方法与实质性的理论，使研究人员能够可靠和有效地测试青少年物质使用的发展理论。