Heterogeneity Among Unobserved Subpopulations

未观察到的亚群之间的异质性

• 批准号: 6795634
• 负责人: BOOIL JO
• 金额: $ 14.4万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6795634
• 关键词: analytical method; classification; clinical research; computer simulation; health science research; human population study; human therapy evaluation; mathematical model; mental health; method development; model design /development; statistics /biometry; therapy compliance

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.

描述（由申请人提供）：拟议的研究项目是一个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，从而弥合统计进步和心理健康研究实践。在心理健康研究中经常提出的一个研究问题是，目标人群中是否有亚组在结果分布、背景特征、发展轨迹和对干预治疗的反应方面存在差异。考虑亚群体差异往往导致对研究结果的解释存在重大差异。当亚群成员完全或部分未观察到时，就会出现统计挑战。由于同时存在的统计挑战，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化。拟议的项目将调查更广泛的统计建模框架，可以反映更现实的设置，同时考虑未观察到的亚群之间的异质性。一般潜变量（GLV）建模将被用作一个灵活的分类工具，捕捉连续和离散的异质性谱。该提案围绕三个具体目标进行组织，以应对心理健康研究中出现的常见并发症：首先，研究方法来估计未观察到的亚群治疗的差异效应。第二，研究方法来建模缺失数据机制，使用未观察到的亚群之间的异质性信息。第三，研究方法来模拟未观察到的子群体之间的异质性占多级数据结构。在追求这些目标时，将采用三种策略：第一，对新的统计模型进行数学研究。其次，通过深入的模拟研究评估这些模型的保真度。最后通过心理健康研究中的实证实例，论证了新模型的适用性和实用性。实证例子中所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。