Methodology for Individualized Comparative Effectiveness Research in Mental Healt

描述(由申请人提供)：比较有效性研究(CER)对改善医疗保健质量具有重大承诺。然而，在履行这一承诺方面，它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，目前的CER框架并不能直接满足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总体平均治疗效果转变为估计个体治疗效果。我们建议将CER领域转向个性化CER(ICER)，并在临床决策点直接满足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，如严重抑郁障碍(MDD)，这些疾病具有显著的不同治疗反应。尽管对MDD的多种治疗方案有50年的经验，但关于它们对个别患者的相对有效性的信息仍然缺乏。在CER目标的指导下，我们将在心理健康研究中概念化一个更好的统计建模框架。我们的方法论基于对具有纵向数据的时变协变量和动态离散选择模型的研究，并使用潜在结果因果推理框架来概念化。具体地说，我们的模型将(1)纳入多种治疗选择和患者的治疗偏好，(2)允许个体未被观察到的异质性，以及(3)在个体水平上产生对治疗替代方案的益处和危害的实际有用的预测性测量。我们将开发连续的、离散的和事件发生时间结果的模型。经验上，我们将使用NIMH资助的缓解抑郁的顺序治疗替代疗法(STAR*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，如多重结果、非随机辍学、审查、缺失项目反应和自我选择。通过预测模型构建过程中的潜在复杂情况，我们将最大限度地利用我们的框架。最终，我们的研究旨在为临床决策生成有用的输入信息。我们设想使用从我们的模型估计的参数来提高未来计算机化决策支持工具的灵活性和个性化。 公共卫生相关性：拟议工作的完成将使比较有效性研究(CER)个体化。它将帮助临床医生根据最好的证据做出决定，这些证据结合了患者的喜好并平衡了益处和危害。新方法还将增加来自传统CER的现有证据的有用性，从而显著提高NIH的投资回报。

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.