Heterogeneity Among Unobserved Subpopulations

描述（由申请人提供）：拟议的研究项目是一位年轻研究者首次提交的R01申请。拟议项目的目标是通过开发和调查新模型来解释未观察到的（潜在的）亚群体之间的异质性，从而将统计进步和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，目标人群中是否存在结果分布、背景特征、发展轨迹和对干预治疗反应不同的亚群。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。当亚种群的隶属关系完全或部分未被观察到时，统计上的挑战就出现了。由于共存的统计挑战，解释潜在亚群（潜在类）之间异质性的统计方法可能会进一步复杂化。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环境，同时考虑到未观察到的亚群体之间的异质性。一般潜在变量（GLV）建模将被用作一种灵活的分类工具，可以捕获连续和离散的异质性谱。针对心理健康研究中出现的常见并发症，该提案围绕三个具体目标进行组织：首先，研究评估未观察亚群治疗差异效果的方法。其次，研究利用未观察到的亚群异质性信息建立缺失数据机制模型的方法。第三，研究在考虑多层次数据结构的未观察亚群之间建立异质性模型的方法。为了实现这些目标，将采用三种策略：首先，对新的统计模型进行数学调查。其次，通过深入的仿真研究来评估这些模型的保真度。最后，通过实证验证新模型在心理健康研究中的适用性和实用性。在实证例子中展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.