Functional relationships in Bayesian evidence synthesis of multiple outcomes

医疗保健政策和临床决策通常基于对既往临床试验证据的系统性审查、批判性评估和统计总结。希望这将提供关于应推荐哪种治疗或哪种治疗的信息，基于它们对特定措施的有效性（例如，患者的疼痛改善更大）。然而，对于许多临床状况，使用几种不同的工具来评估患者对治疗的反应。对这些工具（结果）的治疗效果也可以以不同的格式报告。为英国国家健康与护理卓越研究所（NICE）制定的临床指南通常就是这种情况，其中不同但通常相关的结果被认为对于指导临床实践建议同样重要。对每个结果进行单独的分析，忽略了结果是相关的这一事实，并可能导致不同甚至相互矛盾的结论。因此，最好以连贯的方式总结所有结果的所有证据。这充分利用了现有的证据，并将提供更好（更精确）的信息，作为临床决策和政策建议的基础。目前的方法，总结一个以上的结果的证据是技术上难以实施，往往不会大大提高结果的精度。我们提出了更多的限制性方法，使用专家意见的各种结果是如何相关的，以获得更好的估计治疗效果。例如，在社交焦虑的治疗中，已知如果患者的症状（在特定量表上测量）没有随着治疗而改善，则没有患者将被分类为已经从病症中恢复（在另一个量表上测量）。相反，改善症状的治疗也更有可能使更多的患者康复。我们提出的方法，总结不同的证据，但相关的结果，利用已知的关系，如这些，以获得更好的治疗效果estimations.The建议的方法是技术上更简单的实现比目前提出的方法，总结多个结果。然而，通过将治疗效果与不同结局之间的关系强加于人，我们将对数据做出强有力的假设。将使用推荐的统计技术和专家临床意见检查这些假设，以确保拟定模型是适当的。我们将比较所提出的方法与当前的标准，总结治疗效果的多个结果。我们建议将这些方法应用于不同的临床环境，开发方法来指定结果之间的关系，并与临床同事和决策者合作，以确保假设是合理的，而不是与现有数据相矛盾。我们的目标是开发一种策略，可以遵循，以获得临床意见的合理关系之间的结果，实施他们在一个统计模型，并评估该模型使用现有的统计软件，以检查所作的假设是适当的。

Healthcare policy and clinical decisions are usually based on a systemic review, critical appraisal and statistical summary of the evidence from previous clinical trials. It is hoped that this will provide information on which treatment or treatments should be recommended, based on how effective they are on a particular measure (for example that patients' pain improves by a greater amount). However, for many clinical conditions several different tools are used to assess patients' response to treatment. Treatment effects on each of these tools (outcomes) can also be reported in different formats. This is often the case in clinical guidelines produced for the UK's National Institute for Health and Care Excellence (NICE), where different, but often related, outcomes are considered equally important to guide recommendations for clinical practice. Performing separate analyses on each outcome, ignores the fact that outcomes are related and can lead to different or even conflicting conclusions. It is therefore desirable to summarise all the evidence on all outcomes in a coherent way. This makes best use of available evidence and will provide better (more precise) information on which to base clinical decisions and make policy recommendations. Current methods for summarising evidence on more than one outcome are technically difficult to implement and often do not greatly improve the precision of results. We propose more restrictive methods, which use expert opinion on how the various outcomes are related to obtain better estimates of treatment effects. For example, in the treatment of Social Anxiety, it is known that if patients' symptoms (measured on a particular scale) do not improve with treatment, then no patients will be classified as having recovered from the condition (measured on another scale). Conversely, treatments which improve symptoms are also more likely to have more patients achieving recovery. We propose methods for summarising the evidence on different, but related outcomes, by taking advantage of known relationships such as these, in order to obtain better treatment effect estimates.The proposed methods are technically simpler to implement than the methods currently proposed for summarising multiple outcomes. However, by imposing relationship between the treatment effects on the different outcomes, we will be making strong assumptions about the data. These assumptions will be checked using recommended statistical techniques and expert clinical opinion, to ensure that the proposed models are appropriate. We will compare the proposed methods to the current standards for summarising treatment effects on multiple outcomes.We propose to apply the methods to different clinical settings, develop methods to specify the relationships between outcomes and collaborate with clinical colleagues and decision makers to ensure that the assumptions are sensible and not contradicted by available data. We aim to develop a strategy which can be followed in order to obtain clinical opinion on plausible relationships between the outcomes, implement them in a statistical model and evaluate that model using available statistical software, to check that the assumptions made are appropriate.