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以往临床试验证据的系统审查、关键评估和统计总结。希望这将提供关于哪种或哪种治疗方法应该被推荐的信息，基于它们对特定措施的有效性（例如，患者的疼痛改善程度更大）。然而，对于许多临床情况，使用几种不同的工具来评估患者对治疗的反应。每种工具的治疗效果（结果）也可以以不同的格式报告。在为英国国家健康与护理卓越研究所（NICE）制定的临床指南中经常出现这种情况，不同但往往相关的结果被认为对指导临床实践的建议同样重要。对每个结果进行单独分析，忽略了结果是相关的，可能导致不同甚至相互矛盾的结论。因此，以连贯的方式总结所有结果的所有证据是可取的。这将最大限度地利用现有证据，并将提供更好（更精确）的信息，以作为临床决策和制定政策建议的基础。目前对一个以上结果的证据进行总结的方法在技术上难以实施，而且往往不能大大提高结果的准确性。我们提出了更严格的方法，这些方法使用专家意见来了解各种结果之间的关系，从而更好地估计治疗效果。例如，在社交焦虑的治疗中，众所周知，如果患者的症状（用一种特定的量表衡量）没有随着治疗而改善，那么没有患者会被归类为已经从这种情况中恢复过来（用另一种量表衡量）。相反，改善症状的治疗方法也更有可能使更多的患者康复。我们提出了一些方法，通过利用这些已知的关系来总结不同但相关的结果的证据，以获得更好的治疗效果估计。所提出的方法在技术上比目前提出的用于总结多个结果的方法更容易实现。然而，通过对不同结果施加治疗效果之间的关系，我们将对数据做出强有力的假设。这些假设将使用推荐的统计技术和专家临床意见进行检查，以确保所建议的模型是适当的。我们将比较建议的方法与目前的标准，以总结多种结果的治疗效果。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，不会与现有数据相矛盾。我们的目标是制定一个可以遵循的策略，以便获得关于结果之间合理关系的临床意见，在统计模型中实施它们，并使用可用的统计软件评估该模型，以检查所做的假设是否适当。

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.