Improving statistical methods to address confounding in the economic evaluation of health interventions

世界各地的政策制定者使用卫生经济评估来帮助决定提供哪些卫生保健干预措施。对于药品的成本效益分析，随机试验被视为经济评估的“黄金标准”证据来源，因为随机化确保了治疗患者和对照患者的健康和经济结果之间的任何差异都反映了治疗的因果关系。在许多情况下，包括对新设备和诊断测试的评估、临床指南的制定以及对新的卫生政策倡议的评估，没有相关的试验证据。此类经济评估需要使用非随机证据，例如登记数据或队列研究。对于这些研究，治疗方案之间的随机性不再得到保证。在这里，由于混杂因素，即患者的特征使患者更有可能接受一种治疗而不是另一种治疗，也影响健康结果和最终的治疗成本，治疗组之间的简单比较将在估计的感兴趣的数量中产生选择偏差。如果使用适当的统计方法，可以调整由于混淆引起的选择偏差。然而，正如最近的一次系统审查表明，在已发表的经济评估中，解决这一问题的统计分析的质量并不令人满意。这类研究的结果可能会导致关于成本效益的错误结论，并导致卫生保健资源被错误分配。到目前为止，解决经济评估中选择偏差的方法发展仅限于相对简单的环境，如比较两种处理，这些处理不会随时间而改变。然而，这些设置可能不是更复杂的评估的特征。首先，决策者可能不仅需要关于二元治疗的成本效益的信息，而且还需要关于提供何种强度的治疗的信息。例如，当引入新的财务激励时，政策制定者可能想知道最优激励水平是什么。第二，在临床实践中，提供的治疗可以根据患者的特点进行，例如癌症的治疗根据肿瘤的进展而改变。第三，许多干预措施--通常是卫生政策倡议--是在一个机构(例如NHS信托)或整个地理区域(例如卫生当局)一级实施的，由于缺乏适当的控制小组，针对混淆进行调整可能具有挑战性。目前，缺乏对这些环境的方法论指导。这可能会导致使用不适当的方法，导致严重的估计偏差，或者更糟糕；完全不鼓励分析师和决策者利用非随机证据进行经济评估。能够解决这些环境中的混淆的方法处于因果推理文献发展的前沿，然而这些方法尚未被翻译，并有可能扩展到经济评估的环境中。我建议进行一项全面的研究计划，以解决这一知识差距，使用模拟工作以及来自高度相关的临床和政策领域的数据。这项研究将评估并在必要时推广因果推理文献中的替代方法，以解决经济评估中的混淆问题。这项研究将使我能够向应用研究人员和决策者提供建议，建议哪些方法在经济评估环境中是合适的。通过深入传播这些方法，旨在提高经济评价中统计分析的质量，导致更准确的成本效益结果，为决策提供更有力的证据。因此，这项研究将有助于确保稀缺的资源以最佳方式分配，以改善英国的人口健康。

Policy makers worldwide use health economic evaluation to help decide which health care interventions to provide. For the cost-effectiveness analysis of pharmaceuticals, randomised trials are seen as the "gold standard" source of evidence for economic evaluation, because randomisation ensures that any difference between the health and economic outcomes of the treated and control patients reflects the causal effect of the treatment. In many settings, including the evaluation of new devices and of diagnostic tests, clinical guideline development, and the evaluation of new health policy initiatives, no relevant trial evidence is available. Such economic evaluations need to use non-randomised evidence, for example registry data, or cohort studies. For these studies, randomisation between treatment arms is not ensured any longer. Here, a simple comparison of treatment groups would yield selection bias in the estimated quantity of interest, due to confounding factors, i.e. patient characteristics that make it more likely for a patient to receive one treatment over the other, and also influence the health outcomes and eventual treatment costs. Selection bias due to confounding can be adjusted for if appropriate statistical methods are used. However, as a recent systematic review has demonstrated, in published economic evaluations, the quality of statistical analysis to address this problem is unsatisfactory. Results from such studies can lead to the wrong conclusions on cost-effectiveness, and to health care resources being misallocated. Methods developments for addressing selection bias in economic evaluation so far has been limited to relatively simple settings, such as comparing two treatments, which do not change over time. However, these settings might not characterise more complex evaluations. First, decision makers may require information not just about the cost-effectiveness of a binary treatment, but also about what intensity of treatment to provide. For example, when introducing a new financial incentive, the policy maker may want to know what the optimal level of incentive is. Second, in clinical practice, treatment provided can respond to the patient's characteristics, for example cancer treatment is switched according to tumour progression. Third, many interventions, typically health policy initiatives, are implemented at the level of an institution (e.g. NHS trust) or for an entire geographical region (e.g. health authority), and adjusting for confounding might be challenging due to the lack of an appropriate control group. Currently there is a lack of methodological guidance for these settings. This might lead to the use of inappropriate methods, resulting in severely biased estimates, or worse; completely discourage analysts and decision makers from exploiting non-randomised evidence for economic evaluation. Methods that can address confounding in these settings are at the forefront of developments in the causal inference literature, however these methods have yet to be translated, and potentially extended to the setting of economic evaluation. I propose to conduct a comprehensive research programme to address this gap in knowledge, using both simulation work and data from clinical and policy areas of high relevance. The research will assess and if necessary, extend alternative methods from the causal inference literature for addressing confounding in economic evaluation. This research will enable me to provide recommendations on which methods are appropriate in an economic evaluation setting, towards applied researchers and decision makers. By thorough dissemination of the methods, this research aims to improve the quality of statistical analysis in economic evaluation, leading to more accurate cost-effectiveness results, and a stronger evidence for decision making. This research will therefore help ensure that scarce resources are allocated in the best ways for improving population health in the UK.