Methodology for MH/Drug Prevention and Early Intervention

MH/药物预防和早期干预的方法

• 批准号: 7645176
• 负责人: C. HENDRICKS BROWN
• 金额: $ 50.96万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 1986
• 资助国家: 美国
• 起止时间: 1986-02-01 至 2010-08-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7645176
• 关键词: AIDS/HIV problem; Address; Affect; Alcohol abuse; Alcohol dependence; Area; Attention; Behavior; Clinic; Clinical Trials Design; Communities; Conduct Disorder; Data; Development; Diagnostic Procedure; Disease; Drug abuse; Drug usage; Early intervention trials; Early treatment; Effectiveness; Environment; Event; Failure; Family; Feeling suicidal; Frequencies; General Population; Growth; Intervention; Intervention Studies; Learning; Link; Major Depressive Disorder; Mediation; Mental Health; Mental disorders; Methodology; Methods; Modeling; Outcome; Persons; Pharmaceutical Preparations; Population; Prevention; Prevention Research; Prevention strategy; Preventive; Procedures; Psychopathology; Randomized; Randomized Controlled Clinical Trials; Research; Research Personnel; Risk; Schizophrenia; Schools; Science; Services; Site; Social Work; Staging; Statistical Methods; Statistical Models; Subgroup; Substance Addiction; Substance abuse problem; Suicide; Techniques; Testing; Time; Variant; Violence; Work; accomplished suicide; adolescent substance abuse; base; depression; design; effectiveness trial; flexibility; follow-up; high risk sexual behavior; innovation; mental disorder prevention; method development; model design; prevent; programs; scale up

DESCRIPTION (provided by applicant): This competing continuation R01 for the Prevention Science and Methodology Group (PSMG; R01-MH40859) is to support the development of new statistical methodologies and their application in innovative settings This competing continuation R01 for the Prevention Science and Methodology Group (PSMG; R01-MH40859) is to support the development of new statistical methodologies and their application in innovative settings to the design and analysis of preventive and early intervention trials targeting mental health problems and disorders as well as drug and alcohol dependence and abuse. The analytic methods we have developed over this last five years, including growth mixture models (GMM) and general growth mixture models (GGMM), have provided researchers with a rich set of models to examine intervention impact over time. This application is to develop new analytic methods and new statistical designs to understand the developmental, contextual, and person level influences on intervention outcomes, the mechanisms by which mediation occurs, and the successful implementation and integration of effective preventive and early interventions in communities. This work directly addresses the major research questions now facing the fields of prevention and early intervention research. Our specific aims are: 1. Develop new statistical designs and analytic methods for examining intervention impact across person, environment, and developmental course. These intervention models will be used to examine multilevel mediation/moderation, impact on multiple contexts across time, and variation in impact across subgroups differentiated by their early and developing risk for psychopathology. We will test and refine these methods on existing and new prevention and early intervention trials that target conduct disorder, drug abuse, suicidal ideation and behavior, school failure, depression, and risky sexual behavior. 2. Develop new statistical designs and analytic methods for examining preventive and early intervention impact on low base rate disorders. Special statistical methods are necessary to test prevention strategies for outcomes that are rare. We will develop innovative statistical designs and analytic procedures and examine their ability to test prevention strategies for completed suicide, major depressive disorder, schizophrenia, and other major mental disorders, as well as substance dependence, and HIV/AIDS. 3. Develop new designs and analytic methods for examining impact as interventions are implemented community-wide. In the next five years, we will develop new statistical designs for stages of research that follow tests of effectiveness. These designs will address how interventions and intervention components can best be combined; whether program fidelity and program effects remain high over time, how implementation and outcome are affected as a program is scaled up in a community; and how program adaptation or tailoring to a population can best be studied. We will use existing effectiveness trial data for the prevention of conduct disorder, school failure, and drug abuse to develop and refine these models.

描述（由申请人提供）：预防科学和方法组的这种竞争延续R01（PSMG; R01-MH40859）是支持新的统计方法的开发及其在创新环境中的新统计方法的发展R01，用于预防科学组（PSMG; r01-MH40859）IS的竞争延续R01，以实现R01针对心理健康问题和疾病以及毒品和酒精依赖和虐待的预防和早期干预试验的设计和分析。我们在过去五年中开发的分析方法，包括生长混合模型（GMM）和一般生长混合模型（GGMM），为研究人员提供了丰富的模型，以检查随着时间的推移的干预影响。该应用程序是开发新的分析方法和新的统计设计，以了解对干预结果的发展，上下文和人层面的影响，调解发生的机制以及在社区中的有效预防和早期干预的成功实施和整合。这项工作直接解决了现在的预防和早期干预研究领域面临的主要研究问题。 我们的具体目的是：1。开发新的统计设计和分析方法，以检查跨人员，环境和发展过程的干预影响。这些干预模型将用于检查多级中介/节制，对跨时间的多种环境的影响以及因早期和发展心理病理风险而区分的亚组影响的差异。我们将针对现有和新的预防和早期干预试验进行测试和完善这些方法，这些方法针对行为障碍，滥用药物，自杀念头和行为，学校失败，抑郁和风险性行为。 2。开发新的统计设计和分析方法，以检查预防性和早期干预对低基础率疾病的影响。特殊的统计方法对于预防策略的结果是必不可少的。我们将开发创新的统计设计和分析程序，并研究其测试自杀，重度抑郁症，精神分裂症和其他主要精神障碍的预防策略的能力，以及药物依赖性以及HIV/AIDS。 3。开发新的设计和分析方法，以检查在社区范围内实施干预措施时检查影响。在接下来的五年中，我们将开发新的统计设计，以进行遵循有效性测试的研究阶段。这些设计将解决如何最好地结合干预措施和干预组件；计划保真度和程序效应是否随着时间的流逝而保持较高，随着计划在社区中扩大计划，实施和结果如何受到影响；以及如何最好地研究计划适应或量身定制的计划。我们将使用现有的有效试验数据来预防行为障碍，学校失败和滥用药物，以开发和完善这些模型。