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

随机对照试验证据是公认的评估相对治疗效果的“黄金标准”，为决策提供依据，因为患者被随机分配到干预措施中，这保证了可能与患者结果相关的任何患者特征与分配给他们的干预措施无关(因为这是随机选择的)。因此，被随机分配到不同组的患者只在他们分配的干预措施上有所不同，任何结果的差异都可以归因于单独的干预。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。这使得不同的结果变得不清楚是由于不同的干预措施还是不同的患者特征。合并许多随机试验的结果并获得感兴趣的干预措施的效果的综合测量的方法已经很好地建立了。然而，当随机化的证据在数量或质量上受到限制时，自然会考虑使用非随机化(观察性)数据作为证据的替代或补充来源。纳入观察性证据带来了挑战，因为这种证据可能不能估计干预的真正效果，它可能是有偏见的。根据可用证据的来源和结构，提出了不同的方法来处理这种偏差。然而，到目前为止，还没有就所提议的任何方法达成明确的共识。因此，卫生技术评估机构，特别是像NICE这样的报销机构在评估证据方面面临着越来越多的挑战，并呼吁进行额外的研究。为了响应这一呼吁，我们建议开展五个相互关联的项目：其中四个项目分别侧重于一种不同类型的证据结构。第五个项目开发了检验偏差或潜在偏差对治疗决策的影响的方法。第一个项目将评估在纳入单一干预研究(非比较研究)时产生的治疗效果的误差程度，或者在感兴趣的干预可以分为两组干预的情况下，没有研究将一组中的干预与另一组中的干预进行比较。该项目依赖于至少一项研究的个人参与者数据。第二个项目将考虑个人参与者数据不可用的情况，并将(1)调查各种方法的性质；(2)确定哪种方法在减少不同类型观测数据的决策不确定性方面可能是最好的。第三个项目将进行计算机模拟，其中可以假设处理效果是真实的，并且模拟(计算机生成的)数据真实地代表感兴趣的真实数据，以评估不同方法在什么情况下最可能产生最小偏差的结果。第四个项目专门针对评估新药的有管理的进入。在现有证据仍然有限的情况下，有一种趋势是及早评估新的、有前途的卫生技术。用于为这些决策提供信息的方法做出了强有力的假设。我们将探索常规收集的数据库中可用的观测数据来验证这些假设的可能性。最后一个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一个是最佳治疗方法的决定之前，它必须有多大的偏见？”在这种情况下，决策可以基于，例如，对NHS来说代表最佳金钱价值的治疗。

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.