HOD1: Inferring relative treatment effects from combined randomised and observational data

随机对照试验证据是公认的“黄金标准”，用于估计相对治疗效果，为决策提供信息，因为患者被随机分配到干预措施中，这保证了任何可能与患者结果相关的患者特征与他们被分配的干预措施无关（因为这是随机选择的）。因此，随机分配到不同组的患者仅在分配的干预措施上有所不同，任何结果的差异都可归因于单独的干预措施。在没有随机化的情况下，治疗分配可能基于患者的特征，这也会导致不同的结果。这使得不清楚不同的结果是由于不同的干预措施还是不同的患者特征。结合许多随机试验的结果并获得感兴趣的干预措施效果的综合测量方法已经得到了很好的建立。然而，当随机证据在数量或质量上受到限制时，就像越来越多的情况那样，很自然地考虑使用非随机（观察）数据作为替代或补充证据来源。观察性证据的纳入提出了挑战，因为这些证据可能无法估计干预的真实效果，可能存在偏见。根据现有证据的来源和结构，已经提出了不同的方法来处理这种偏差。然而，到目前为止，对所提出的任何方法都没有达成明确的共识。因此，卫生技术评估机构，特别是诸如NICE这样的报销机构在评估证据方面面临越来越大的挑战，并呼吁进行更多的研究。为了回应这一呼吁，我们建议开展五个相互关联的项目：其中四个侧重于不同类型的证据结构。第五个项目开发了检验偏倚或潜在偏倚对治疗决策影响的方法。第一个项目将评估包括单一干预研究（非比较研究）时产生的治疗效果的误差程度，或者将感兴趣的干预措施分为两组干预措施，而没有研究将一组干预措施与另一组干预措施进行比较。该项目依赖于至少一项研究中的个人参与者数据。第二个项目将考虑个人参与者数据不可用的情况，并将(1)调查各种方法的特性；(2)对于不同类型的观测数据，确定哪一种方法可能最能降低决策不确定性。第三个项目将进行计算机模拟，其中可以假设“真实”的治疗效果是已知的，并且模拟（计算机生成的）数据现实地代表了感兴趣的真实数据，以评估不同方法最有可能产生最小偏差结果的情况。第四个项目专门面向评估新药的管理进入。有一种趋势是，在现有证据仍然有限的情况下，尽早评价新的、有前途的卫生技术。为这些决策提供信息的方法有很强的假设。我们将探索常规收集数据库中观测数据的潜力，以验证这些假设。最后的项目将扩展现有的方法来回答这个问题：“假设这个证据是有偏见的，在它改变我们关于哪种是最好的治疗方法的决定之前，它必须有多大的偏见？”这个决定可以基于，例如，对NHS来说，最物有所值的治疗方法。

Randomised controlled trial evidence is the recognized "gold-standard" to estimate relative treatment effects to inform decision making, because patients are allocated to interventions randomly which guarantees that any patient characteristics that may be related to the patients' outcome are not related to the intervention they were allocated (since this was randomly chosen). Thus, patients randomised to different groups differ only in their allocated intervention and any difference in outcomes can be attributed to the intervention alone.In the absence of randomisation, treatment assignment may be based on patient characteristics that also lead to a different outcome. This makes it unclear whether the different outcomes were due to the different interventions or to different patient characteristics.Methods for combining the results of many randomised trials and obtaining a combined measure of the effect of the interventions of interest are well established. However, when randomised evidence is limited in quantity or quality, as is increasingly the case, it is natural to consider the use of non-randomised (observational) data as a substitute or supplementary source of evidence. The inclusion of observational evidence raises challenges as this evidence may not be estimating the true effect of the intervention, it may be biased.Different methods have been proposed to handle this bias according to the origin and structure of the evidence available. However, so far, no clear consensus has emerged regarding any of the methods proposed. Thus, Health Technology Assessment bodies and particularly reimbursement agencies such as NICE face increasing challenges in their assessments of evidence and have called for extra research. To answer this call, we propose to carry out five interlinked projects: four of them are focused each on a different type of evidence structure. A fifth project develops methods that examine the impact of bias, or potential bias, on the treatment decision.The first project will evaluate the degree of error in the treatment effects produced when including studies with a single intervention (non-comparative studies), or where the interventions of interest can be separated into two groups of interventions where no study has compared an intervention in one group with an intervention in the other group. This project relies on there being individual participant data from at least one of the studies. The second project will consider cases where the individual participant data are not available and will (1) investigate the properties of the various methods; (2) determine which of the methods is likely to be best at reducing decision uncertainty for different kinds of observational data.The third project will carry out computer simulations where the 'true' treatment effects can be assumed to be known and where the simulated (computer generated) data are realistically representative of the real data of interest to assess the circumstances under which the different methods are most likely to produce the least biased results. The fourth project is specifically oriented to assess managed entry of new pharmaceuticals. There is a trend towards appraising new, promising, health technologies early, when available evidence is still limited. Methods used to inform these decisions make strong assumptions. We will explore the potential for observational data available in routinely collected databases to validate these assumptions.The final project will extend existing methods to answer the question: "suppose this evidence is biased, how biased would it have to be before it changed our decision as to which is the best treatment?" where the decision can be based on, for example, the treatment which represents best value for money to the NHS.