Extensions and Applications of Group-Based Models of Development and the Software for Estimating Them

发展轨迹描述了行为随年龄或时间的变化过程。 该项目建立在先前NSF资助的研究（SES-9911370 SES-9511412）的基础上，该研究开发了一种基于组的方法，用于识别感兴趣人群中的不同个体轨迹组。 作为这项研究的一部分，开发了免费分发的“罐装”SAS软件（Proc Traj），用于估计轨迹模型。 该项目将解决三个主要问题。 首先，所有的纵向研究，特别是对人的纵向研究，都存在缺失评估的问题，因为受试者因死亡或无法找到或不再愿意参与等原因退出研究。 由于受试者辍学不是随机的，研究的一个重要目标是扩展基本模型，以考虑不同的辍学率轨迹组。 这一改进将纳入Proc Traj软件。 其次，先前的工作将基于群体的轨迹建模与倾向分数和匹配的工作联系起来。 这种联系的目的是对主要生活事件或治疗干预对行为轨迹的影响做出（更自信的）因果推断。 群体成员的后验概率是基于群体的轨迹建模的关键产物，在连接这两条研究路线方面发挥着核心作用。 本研究的第二个重要目的是探索后验概率在治疗组和对照组之间测量协变量的平衡中的贡献。 第三，出于易处理性的原因，用Proc Traj估计的轨迹模型假设轨迹组内的时间段之间的条件独立性。 本研究也将探讨此假设的失败对参数估计及其标准误差的影响。方法的扩展将被用来调查发展精神病理学和发展犯罪学的重要实质性问题。 在这方面，因果推理的工作有一个重要的应用领域，在评估非随机分配的医疗，包括精神咨询和药物治疗的有效性，并在分析事件的影响，如监禁对随后的犯罪行为。 应用扩展来解释非随机辍学可能会揭示不考虑这一现象的调查结果中的重要偏见。 此外，Proc Traj的免费广泛传播使其他研究人员能够更容易地利用这项研究的方法进步。 值得注意的是，许多博士。候选人和博士后学者使用Proc Traj。 因此，这项研究有助于下一代研究人员的教育。

A developmental trajectory describes the course of a behavior over age or time. This project builds upon prior NSF-funded research (SES-9911370 & SES-9511412) that developed a group-based approach for identifying distinctive groups of individual trajectories within the population of interest. As part of that research, "canned" SAS-based software (Proc Traj), distributed free of charge, was developed for estimation of the trajectory models. This project will address three major issues. First, all longitudinal studies, particularly of people, suffer from the problem of missing assessments because subjects drop out of the study for reasons such as death or because they can not be located or are no longer willing to participate. Because subject drop-out is not random, one important objective of the research is to extend the basic model to account for differential drop-out rates by trajectory group. This enhancement will be incorporated into the Proc Traj software. Second, prior work links group-based trajectory modeling with work on propensity scores and matching. The aim of this linkage is to make (more confident) causal inferences about the effect of major life events or therapeutic interventions on trajectories of behavior. The posterior probabilities of group membership, a key product of group-based trajectory modeling, play a central role in linking these two lines of research. A second important objective of the research is to explore the contribution of the posterior probabilities in creating balance on measured covariates between treated and controls. Third, for reasons of tractability, trajectory models estimated with Proc Traj assume conditional independence across time periods within trajectory group. This research also will explore the impact of this assumption's failure on parameter estimates and their standard errors. The methodological extensions will be used to investigate important substantive questions in developmental psychopathology and developmental criminology. In this regard, the work on causal inference has an important application domain in assessing the effectiveness of nonrandomly assigned medical treatments, including psychiatric counseling and drug treatment, and in analyzing the impact of events such as incarceration on subsequent criminality. Application of the extension to account for non-random drop-out may reveal important biases in findings that do not take this phenomenon into account. Also, wide dissemination of Proc Traj free of charge allows other researchers to more easily utilize the methodological advances of this research. It is important to note that many Ph.D. candidates and postdoctoral scholars use Proc Traj. Thus, this research contributes to the education of the next generation of researchers.