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Methodology for Individualized Comparative Effectiveness Research in Mental Healt

心理健康个体化比较有效性研究方法

基本信息

批准号：
8037874
负责人：
Zhehui Luo
金额：
$ 110.96万
依托单位：
MICHIGAN STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-30 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8037874
关键词：
Address Biometry Caring Characteristics Chronic Disease Clinical Clinical Trials Comorbidity Coupled Data Decision Aid Decision Making Development Disease Disease Management Dropout Effectiveness Effectiveness of Interventions Elements Epidemiology Equilibrium Event Evidence Based Medicine Expenditure Experimental Designs Face Foundations Funding Future Goals Healthcare Heterogeneity Individual Individuality Institute of Medicine (U.S.)Intervention Intervention Studies Investments Major Depressive Disorder Measures Medical Mental Depression Mental Health Meta-Analysis Methodology Methods Modeling National Institute of Mental Health Nature Observational Study Outcome Patient Preferences Patients Population Prevention Psyche structure Psychiatry Randomized Controlled Trials Relative (related person)Research Research Methodology Research Priority Severities Sociology Statistical Models Surveys Target Populations Time United States Agency for Healthcare Research and Quality United States National Institutes of Health Work abstracting alternative treatment base comparative effectiveness computerized effectiveness research evidence base experience flexibility health care quality health information technology improved meetings predictive modeling preference productivity loss response simulation systematic review tool treatment effect treatment response

项目摘要

DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) holds significant promise to improve health care quality. Nevertheless, it faces some significant methodological challenges in fulfilling this promise. Ongoing research addresses some of these challenges. However, the current CER framework does not directly meet the stated goal of CER, i.e., identifying "which interventions are most effective for which patient under specific circumstances." To do so requires a shift of research paradigm from estimating population average treatment effects to estimating individual treatment effects. We propose to move the field of CER toward individualized CER (iCER) and directly address the need of clinicians and patients at the clinical decision point. The potential use for iCER is most prominent for chronic illnesses, such as major depressive disorder (MDD), that have significant heterogeneous treatment responses. Despite of 50 years of experience with many treatment options for MDD, information on their relative effectiveness for individual patients is still lacking. Guided by the goal of CER, we will conceptualize an iCER statistical modeling framework in mental health research. Our methodology is grounded in research on time-varying covariates and dynamic discrete choice models with longitudinal data; and it is conceptualized using the potential outcomes causal inference framework. Specifically, our models will (1) incorporate multiple treatment options and the patient's treatment preferences, (2) allow individual unobserved heterogeneity, and (3) generate practically useful predictive measures of benefits and harms of treatment alternatives at the individual level. We will develop models for continuous, discrete, and time-to-event outcomes. Empirically, we will use the NIMH funded Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial and the AHRQ supported Medical Expenditure Panel Survey (MEPS) data to evaluate our methods. We will incorporate the typical features of mental health intervention studies and observational studies, such as multiple outcomes, nonrandom dropout, censoring, missing item-responses and self-selection. By anticipating potential complications in model building we will maximize the usefulness of our framework. Ultimately, our research aims at generating useful input information for clinical decisions. We envision using parameters estimated from our models to improve the flexibility and individuality of computerized decision support tools in the future. PUBLIC HEALTH RELEVANCE: The completion of the proposed work will individualize comparative effectiveness research (CER). It will assist clinicians in making decisions based on the best evidence that incorporates the patient's preferences and balances benefits and harms. The new methods will also increase the usefulness of existing evidence from the traditional CER and thus significantly increase the return on investment by NIH.

描述（由申请人提供）：比较有效性研究（CER）对提高医疗保健质量具有重要意义。然而，它在实现这一承诺方面面临着一些重大的方法挑战。正在进行的研究解决了其中一些挑战。然而，当前CER框架并不直接满足CER的既定目标，即，确定“在特定情况下，哪些干预措施对哪些患者最有效。“要做到这一点，需要将研究范式从估计群体平均治疗效果转变为估计个体治疗效果。我们建议将CER领域向个体化CER（iCER）方向发展，并在临床决策点直接解决临床医生和患者的需求。iCER的潜在用途最突出的是慢性疾病，如重度抑郁症（MDD），具有显著的异质性治疗反应。尽管有50年的经验，许多治疗选择的MDD，信息的相对有效性为个别患者仍然缺乏。在CER目标的指导下，我们将概念化心理健康研究中的iCER统计建模框架。我们的方法是基于对时变协变量和动态离散选择模型与纵向数据的研究，它是概念化的潜在结果因果推理框架。具体而言，我们的模型将（1）纳入多种治疗方案和患者的治疗偏好，（2）允许个体未观察到的异质性，（3）在个体水平上生成治疗替代方案的益处和危害的实用预测指标。我们将开发连续，离散和时间到事件的结果模型。从经验上讲，我们将使用NIMH资助的缓解抑郁症的序贯治疗替代方案（星星 *D）试验和AHRQ支持的医疗支出小组调查（MEPS）数据来评估我们的方法。我们将结合心理健康干预研究和观察性研究的典型特征，如多个结果，非随机脱落，删失，缺失项目反应和自我选择。通过预测模型构建中的潜在复杂性，我们将最大限度地提高框架的实用性。最终，我们的研究旨在为临床决策提供有用的输入信息。我们设想使用我们的模型估计的参数，以提高灵活性和个性化的计算机化决策支持工具在未来。公共卫生相关性：拟议工作的完成将使比较有效性研究（CER）个性化。它将帮助临床医生根据最佳证据做出决策，这些证据结合了患者的偏好并平衡了益处和危害。新方法还将增加传统CER现有证据的有用性，从而显着增加NIH的投资回报。