Heterogeneity Among Unobserved Subpopulations

描述(由申请人提供)：建议的研究项目是由一名年轻的研究人员首次提交的R01申请。拟议项目的目标是通过开发和调查新的模型来解释未观察到的(潜在的)亚群之间的异质性，从而将统计进展与心理健康研究实践联系起来。心理健康研究中经常提出的一个研究问题是，在目标人群中是否存在在结果分布、背景特征、发展轨迹和对干预治疗的反应方面不同的亚组。考虑亚群差异往往会导致对研究结果的解释出现重大差异。当完全或部分没有观察到亚群体成员时，就会出现统计上的挑战。由于共同存在的统计挑战，解释潜在亚群(潜在类别)之间异质性的统计方法可能会进一步复杂。拟议的项目将调查更广泛的统计建模框架，这些框架可以反映更现实的情况，同时考虑到未观察到的亚群的异质性。一般潜变量(GLV)建模将被用作一种灵活的分类工具，既可以捕捉连续的异质性光谱，也可以捕捉离散性光谱。该提案围绕三个具体目标制定，以应对心理健康研究中出现的常见并发症：第一，调查评估治疗对未观察到的亚群的不同影响的方法。第二，研究利用未观察到的子群体之间的异质性信息对缺失数据机制进行建模的方法。第三，研究考虑多层数据结构的未观察到的子群体之间的异质性的建模方法。在实现这些目标时将采用三种策略：第一，对新的统计模型进行数学调查。其次，通过密集的仿真研究来评估这些模型的逼真度。最后，通过在心理健康研究中的实证案例，论证了新模型的适用性和实用性。实证例子中显示的统计建模特征不仅对结果分析有影响，而且对心理健康研究的研究设计策略也有影响。

DESCRIPTION (provided by applicant): The proposed research project is a first submission of an R01 application by a young investigator. The goal of the proposed project is to bridge statistical advances and mental health research practice by developing and investigating new models to account for heterogeneity among unobserved (underlying) subpopulations. A research question often raised in mental health research is whether there are subgroups within the target population that differ in outcome distributions, background characteristics, developmental trajectories, and response to intervention treatments. Considering subpopulation differences often leads to major differences in the interpretation of research findings. Statistical challenges arise when subpopulation membership is completely or partly unobserved. Statistical methods to account for heterogeneity among latent subpopulations (latent classes) can be further complicated due to co-existing statistical challenges. The proposed project will investigate broader statistical modeling frameworks that can reflect more realistic settings while accounting for heterogeneity among unobserved subpopulations. General latent variable (GLV) modeling will be utilized as a flexible classification tool that captures both the continuous and the discrete spectrum of heterogeneity. The proposal is organized around three specific aims formulated in response to common complications that arise in mental health research: First, investigate methods to estimate differential effects of treatments for unobserved subpopulations. Second, investigate methods to model missing-data mechanisms using information on heterogeneity among unobserved subpopulations. Third, investigate methods to model heterogeneity among unobserved subpopulations accounting for multilevel data structures. Three strategies will be employed in pursuing these aims: First, perform mathematical investigations of new statistical models. Second, evaluate the fidelity of these models through intensive simulation studies. Finally, demonstrate applicability and practicality of new models through empirical examples in mental health research. Statistical modeling features demonstrated in empirical examples will have implications not on y in outcomes analysis, but also in study design strategies for mental health research.