Cross Design Synthesis: Combining Evidence About Antidepressants and Suicidality

描述(由申请者提供)：每一项科学调查都是在有关感兴趣的问题的不确定证据的背景下进行的。我们认为，研究综合的主要目标是使关于这些问题的不同意见达成共识。我们应用的重点是关于抗抑郁药使用和自杀之间存在关联的证据的问题，并建立在我们对FDA关于青少年抗抑郁药使用和自杀的荟萃分析的重新分析(Kaizar，Greenm，Kelleher，Seltman 2005)的基础上。我们工作的动机是认识到，现有的能够解释这个问题的证据基础既可以从实验研究中获得，也可以从非实验研究中获得，而且既不完美，也不完美，因为实验可能是严格的，但受到限制，而非实验研究更一般，但可能是有偏见的，也可能是完整的，因为感兴趣的可用结果可能是间接的(例如，自杀意念/行为和未完成自杀)。这项应用的主要工作是进行一项研究综述，以帮助我们加深对抗抑郁药使用、自杀和自杀之间关系的理解。具体地说，我们将使用交叉设计合成，这是一种相对较新的研究策略，将来自研究的数据与互补设计相结合，以评估和结合来自随机试验和非实验研究的证据。我们的初步合成将包括接受和不接受心理治疗的抗抑郁药的随机试验，以及至少两项包括抗抑郁药使用的较大研究的数据。我们尤其关注有助于综合来自多个数据来源的证据的方法的开发和应用，包括调整影响其外部有效性的因素的研究的方法，以及探索影响内部有效性的偏差的影响的方法。我们的方法将基于贝叶斯分层模型的使用。当综合不仅需要对研究内部和研究之间的异质性建模，而且需要研究类型的定性差异时，我们专注于贝叶斯分层模型作为一种解决方案来进行推理。贝叶斯方法还将有助于促进敏感性分析，以评估各种模型输入的推论的稳健性。我们还将开发可访问和用户友好的统计软件程序，以实施我们开发的方法。

DESCRIPTION (provided by applicant): Every scientific investigation takes place in the context of uncertain evidence about a question of interest. We believe that the primary goal of research synthesis is to bring differing opinions concerning such questions to consensus. The focus of our application is the question regarding the evidence for an association between antidepressant use and suicidality and builds on our reanalysis of the FDA meta-analysis of antidepressant use and suicidality in youth (Kaizar, Greenhouse, Kelleher, Seltman 2005). Our work is motivated by the recognition that the existing evidence base that can inform this question is available from both experimental and non-experimental studies, and is neither perfect, in the sense that experiments may be rigorous but restricted, and non-experimental studies more general but may be biased nor complete, in the sense that the available outcomes of interest may be indirect (e.g., suicidal ideation/behavior and not completed suicide). The primary work of this application is the conduct of a research synthesis to help advance our understanding of the relationship among antidepressant use, suicide, and suicidality. Specifically, we will use cross-design synthesis, a relatively new research strategy for combining data from studies with complementary designs, to evaluate and combine evidence from both randomized trials and non-experimental studies. Our primary synthesis will include randomized trials of antidepressants both with and without psychotherapy along with data from at least two larger studies that include antidepressant use. We are particularly concerned with the development and application of methods that will facilitate the combining of evidence from multiple data sources, including methods for adjusting studies for factors that affect their external validity, and methods for exploring the effects of biases that affect internal validity. Our approach will be based on the use of Bayesian hierarchical models. We focus on Bayesian hierarchical models as one solution to the problem of making inferences when syntheses require not only modeling of within and between study heterogeneity, but also the qualitative differences of study types. The Bayesian approach will also help facilitate sensitivity analyses for assessing the robustness of inferences to various model inputs. We will also develop accessible and user friendly statistical software programs to implement the methodology that we develop.