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.

该应用程序寻求对统计学家，经济学家和临床医生团队的持续支持，以合作开发和应用纵向分层离散选择模型，以了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来了解治疗采用或创新的动态，以及治疗拒绝或experiment，研究人员将更深入地了解常规护理治疗的纵向模式。 在具体目标1中，我们将扩展基于似然的方法，以适应灵活的动态离散选择扩散模型。 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。具体目标2将扩展目标1，以包括精神健康治疗技术传播的地理差异。 这将允许估计地理特定的扩散效应。 在具体目标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。 这涉及使用估计量，不混淆协变量效应，同时考虑患者、提供者、产品和市场之间的变化，从而允许无偏推断。 我们将这些方法应用于抑郁症、双相情感障碍和精神分裂症患者队列，使用多种数据来源。 一个由统计学、经济学和精神病学领导人组成的咨询委员会将每年召开一次会议，以验证方法，并确保将技术融入精神卫生服务研究。 这项研究在方法上的进步将使研究人员、政策制定者和方法学家能够更好地描述影响技术创新/创新的因素，并扩展常规护理的推论。