Modeling Treatment Use & Effectiveness In Mental Illness

建模治疗用途

• 批准号: 6985034
• 负责人: SHARON-LISE Teresa NORMAND
• 金额: $ 49.52万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-02-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6985034
• 关键词: behavioral /social science research tag; bipolar depression; clinical research; geographic difference; human data; major depression; mathematical model; mental disorder chemotherapy; model design /development; psychotherapy; schizophrenia; statistics /biometry; therapy compliance

This application seeks continued support for a team of statisticians, economists, and clinicians to collaborate on the development and application of longitudinal hierarchical discrete choice models for understanding diffusion of mental health technologies and for causal inference. By developing better statistical models for understanding the dynamics of treatment adoption, or innovation, and of treatment rejection, or exnovation, researchers will gain greater insight into longitudinal patterns of usual care treatments. In Specific Aim 1 we will extend likelihood-based approaches to accommodate a flexible family of dynamic discrete choice diffusion models. This will permit the study of the effects of patient, provider, product, and market characteristics on technology adoption or rejection. Specific Aim 2 will extend Aim 1 to include geographic variation in the diffusion of mental health treatment technologies. This will permit estimation of geographic specific diffusion effects. In Specific Aim 3 we will extend Aim 1 to study the causal effect of patient, provider, product, or market characteristics on diffusion. This involves the use of estimators that unconfound covariate effects while accounting for within- and between-patient, provider, product, and market variation, thereby permitting unbiased inferences. We will apply these methods to cohorts of patients with depression, bipolar affective disorder, and schizophrenia using multiple sources of data. An Advisory Board comprised of leaders in statistics, economics, and psychiatry will convene annually to validate methods and ensure integration of techniques into mental health services research. The methodological advances from this research will enable researchers, policy makers, and methodologists to better characterize factors impacting technology innovation/exnovation and to expand the inferences for usual care.

该应用程序为统计学家，经济学家和临床医生团队提供了持续的支持，以合作开发和应用纵向等级离散选择模型，以理解心理健康技术的扩散和因果推断。 通过开发更好的统计模型，以了解采用治疗，创新，治疗排斥或进行循环的动态，研究人员将对常规护理治疗的纵向模式有了更深入的了解。 在特定的目标1中，我们将扩展基于可能性的方法，以适应一个动态离散选择扩散模型的灵活家族。 这将允许研究患者，提供者，产品和市场特征对技术采用或拒绝的影响。特定目标2将扩展目标1，以包括心理健康治疗技术扩散的地理差异。 这将允许估计地理特定的扩散效应。 在特定目标3中，我们将扩展目标1，以研究患者，提供者，产品或市场特征对扩散的因果影响。 这涉及使用估计器，这些估计量在考虑到患者内部，提供商，产品和市场差异时，无形的协变量效应，从而允许无偏见的推论。 我们将使用多种数据来源将这些方法应用于患有抑郁症，双相情感障碍和精神分裂症患者的人群。 由统计，经济学和精神病学领导者组成的顾问委员会每年都会召集，以验证方法并确保将技术整合到心理健康服务研究中。 这项研究的方法论进步将使研究人员，政策制定者和方法论家能够更好地表征影响技术创新/开发的因素，并扩大通常护理的推论。