Cross Design Synthesis: Combining Evidence About Antidepressants and Suicidality

描述（由申请人提供）：每一项科学调查都是在关于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有关这些问题的不同意见达成共识。我们申请的重点是关于抗抑郁药使用与自杀倾向之间相关性证据的问题，并建立在我们对FDA关于青年人抗抑郁药使用与自杀倾向的荟萃分析的重新分析基础上（Kaizar，Greenhouse，Kelleher，Seltman 2005）。我们的工作的动机是认识到，可以告知这个问题的现有证据基础可以从实验和非实验研究中获得，并且在实验可能严格但受限制的意义上，既不完美，也不完美，而非实验研究更一般，但可能有偏见也不完整，在这个意义上，感兴趣的可用结果可能是间接的（例如，自杀意念/行为和未完成的自杀）。本申请的主要工作是进行研究综合，以帮助我们进一步了解抗抑郁药使用，自杀和自杀倾向之间的关系。具体来说，我们将使用交叉设计合成，一个相对较新的研究策略，结合研究数据与互补设计，以评估和联合收割机证据从随机试验和非实验研究。我们的初步综合将包括抗抑郁药的随机试验，包括有和没有心理治疗，沿着至少两个较大的研究，包括抗抑郁药的使用数据。我们特别关注的方法，这将有助于从多个数据源的证据相结合的发展和应用，包括调整研究的影响其外部效度的因素的方法，以及探索影响内部效度的偏见的影响的方法。我们的方法将基于贝叶斯分层模型的使用。我们专注于贝叶斯分层模型作为一个解决方案，使推理的问题时，合成不仅需要建模的研究异质性，而且研究类型的定性差异。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断的稳健性。我们还将开发易于使用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.