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

世界各地的决策者使用卫生经济学评估来帮助决定提供哪些卫生保健干预措施。对于药物的成本效益分析，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效应。 在许多情况下，包括新器械和诊断试验的评价、临床指南的制定以及新卫生政策倡议的评价，都没有相关的试验证据。这种经济学评价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，不再确保治疗组之间的随机化。在这里，由于混杂因素，即患者特征使患者更有可能接受一种治疗而不是另一种治疗，并且还影响健康结果和最终治疗成本，治疗组的简单比较将在估计的感兴趣量中产生选择偏倚。如果使用适当的统计方法，可以调整由于混杂引起的选择偏倚。然而，正如最近的一项系统审查所表明的那样，在已发表的经济评价中，解决这一问题的统计分析的质量不能令人满意。这些研究的结果可能导致对成本效益的错误结论，并导致医疗资源分配不当。 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设置，例如比较两种治疗方法，这些方法不会随着时间的推移而改变。但是，这些设置可能无法支持更复杂的评估。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治疗强度的信息。例如，在引入新的财政激励措施时，决策者可能想知道激励措施的最佳水平是什么。第二，在临床实践中，提供的治疗可以响应患者的特征，例如癌症治疗根据肿瘤进展进行转换。第三，许多干预措施，通常是卫生政策举措，是在机构（如NHS信托）或整个地理区域（如卫生当局）的水平上实施的，由于缺乏适当的对照组，调整混杂因素可能具有挑战性。 目前缺乏对这些环境的方法指导。这可能会导致使用不适当的方法，导致严重偏倚的估计，或者更糟;完全阻止分析师和决策者利用非随机证据进行经济评估。在这些环境中，可以解决混淆的方法是在因果推理文献的发展的最前沿，但这些方法还没有被翻译，并可能扩展到经济评估的设置。我建议进行一项全面的研究计划，以解决这一知识差距，同时使用模拟工作和数据，从临床和政策领域的高度相关性。本研究将评估并在必要时扩展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提供建议，哪些方法是适当的经济评估设置，对应用研究人员和决策者。本研究的目的是通过对这些方法的深入推广，提高经济评价中统计分析的质量，使经济评价的成本效益结果更加准确，为决策提供更有力的依据。因此，这项研究将有助于确保以最佳方式分配稀缺资源，以改善联合王国的人口健康。

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.