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和自杀之间关联的证据问题，并建立在我们对FDA抗抑郁药使用和青少年自杀的meta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现有的证据基础可以从实验和非实验研究中得到这个问题，并且并不完美，因为实验可能是严格的但有限的，而非实验研究更普遍，但可能有偏见，也不完整，因为可用的结果可能是间接的（例如，自杀意念/行为和未完成的自杀）。本应用程序的主要工作是进行一项研究综合，以帮助提高我们对抗抑郁药使用，自杀和自杀行为之间关系的理解。具体而言，我们将使用交叉设计综合，这是一种相对较新的研究策略，用于将研究数据与互补设计相结合，以评估和结合随机试验和非实验研究的证据。我们的初步综合将包括抗抑郁药的随机试验，包括与心理治疗和不治疗的抗抑郁药，以及至少两项包括抗抑郁药使用的大型研究的数据。我们特别关注的方法的发展和应用，这些方法将促进来自多个数据源的证据的结合，包括调整影响其外部有效性的因素的研究方法，以及探索影响其内部有效性的偏差影响的方法。我们的方法将基于贝叶斯层次模型的使用。当综合研究不仅需要对研究内部和研究之间的异质性进行建模，而且还需要对研究类型的定性差异进行建模时，我们将重点放在贝叶斯层次模型上，作为进行推断问题的一种解决方案。贝叶斯方法也将有助于促进敏感性分析，以评估对各种模型输入的推断的鲁棒性。我们还将开发易于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.