Improving statistical methods to address confounding in the economic evaluation of health interventions
改进统计方法以解决健康干预措施经济评估中的混杂问题
基本信息
- 批准号:MR/L012332/2
- 负责人:
- 金额:$ 6.27万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Fellowship
- 财政年份:2016
- 资助国家:英国
- 起止时间:2016 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
世界各地的决策者使用卫生经济评价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证据的“黄金标准”来源,因为随机化确保了治疗组和对照组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,包括评估新器械和诊断测试、制定临床指南以及评估新的卫生政策举措,都没有相关的试验证据。这种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,不再保证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费用,对治疗组进行简单的比较将在估计感兴趣的数量中产生选择偏差。如果使用适当的统计方法,可以调整由混杂引起的选择偏差。然而,正如最近的一项系统综述所表明的那样,在已发表的经济评估中,用于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导致关于成本效益的错误结论,并导致卫生保健资源分配不当。迄今为止,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,例如比较两种处理方法,这些方法不会随着时间的推移而改变。然而,这些设置可能不具有更复杂评估的特征。首先,决策者可能不仅需要二元治疗的成本效益信息,还需要提供治疗强度的信息。例如,当引入一种新的财政激励时,政策制定者可能想知道什么是最优的激励水平。其次,在临床实践中,所提供的治疗可以响应患者的特征,例如根据肿瘤进展切换癌症治疗。第三,许多干预措施,通常是卫生政策举措,是在一个机构(如NHS信托)或整个地理区域(如卫生当局)一级实施的,由于缺乏适当的对照组,对混杂因素进行调整可能具有挑战性。目前缺乏针对这些情况的方法指导。这可能导致使用不适当的方法,造成严重偏差的估计,或更糟;完全阻止分析师和决策者利用非随机证据进行经济评估。在因果推理文献中,能够解决这些环境中混淆的方法处于发展的前沿,然而,这些方法尚未被翻译,并可能扩展到经济评估的设置。我建议开展一项全面的研究计划,利用模拟工作和来自高度相关的临床和政策领域的数据来解决这一知识差距。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向应用研究人员和决策者提供关于哪些方法适合经济评估设置的建议。本研究旨在通过对方法的深入传播,提高经济评估中统计分析的质量,从而获得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以改善联合王国人口健康的最佳方式分配稀缺资源。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Learning From an Association Analysis Using Propensity Scores.
使用倾向得分从关联分析中学习。
- DOI:10.1097/pcc.0000000000002842
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Kreif N
- 通讯作者:Kreif N
Data-adaptive doubly robust instrumental variable methods for treatment effect heterogeneity
用于治疗效果异质性的数据自适应双稳健工具变量方法
- DOI:10.48550/arxiv.1802.02821
- 发表时间:2018
- 期刊:
- 影响因子:0
- 作者:DiazOrdaz Karla
- 通讯作者:DiazOrdaz Karla
Oxford Research Encyclopedia of Economics and Finance
牛津研究经济与金融百科全书
- DOI:10.1093/acrefore/9780190625979.013.256
- 发表时间:2019
- 期刊:
- 影响因子:0
- 作者:Kreif N
- 通讯作者:Kreif N
Propensity score methods for comparative-effectiveness analysis: A case study of direct oral anticoagulants in the atrial fibrillation population.
- DOI:10.1371/journal.pone.0262293
- 发表时间:2022
- 期刊:
- 影响因子:3.7
- 作者:Ciminata G;Geue C;Wu O;Deidda M;Kreif N;Langhorne P
- 通讯作者:Langhorne P
Estimating the Comparative Effectiveness of Feeding Interventions in the Pediatric Intensive Care Unit: A Demonstration of Longitudinal Targeted Maximum Likelihood Estimation.
- DOI:10.1093/aje/kwx213
- 发表时间:2017-12-15
- 期刊:
- 影响因子:5
- 作者:Kreif N;Tran L;Grieve R;De Stavola B;Tasker RC;Petersen M
- 通讯作者:Petersen M
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Noemi Kreif其他文献
Overview of Parametric Survival Analysis for Health-Economic Applications
- DOI:
10.1007/s40273-013-0064-3 - 发表时间:
2013-05-15 - 期刊:
- 影响因子:4.600
- 作者:
K. Jack Ishak;Noemi Kreif;Agnes Benedict;Noemi Muszbek - 通讯作者:
Noemi Muszbek
Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity
用于交错双重差异和动态治疗效果异质性的机器学习
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Julia Hatamyar;Noemi Kreif;Rudi Rocha;Martin Huber - 通讯作者:
Martin Huber
EE84 Cost-Effectiveness Analysis of Xanomeline and Trospium Chloride for Schizophrenia in the United States
EE84 在美国用于精神分裂症的 xanomeline 和氯化托烷司琼的成本效益分析
- DOI:
10.1016/j.jval.2025.04.376 - 发表时间:
2025-07-01 - 期刊:
- 影响因子:6.000
- 作者:
Rachel Kneitel;Noemi Kreif - 通讯作者:
Noemi Kreif
Health facility quality peer effects: Are financial incentives necessary?
卫生设施质量的同群效应:经济激励是必要的吗?
- DOI:
10.1016/j.regsciurbeco.2025.104091 - 发表时间:
2025-05-01 - 期刊:
- 影响因子:2.900
- 作者:
Finn McGuire;Rita Santos;Peter C. Smith;Nicholas Stacey;Ijeoma Edoka;Noemi Kreif - 通讯作者:
Noemi Kreif
Noemi Kreif的其他文献
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{{ truncateString('Noemi Kreif', 18)}}的其他基金
Tailoring health policies to improve outcomes using machine learning, causal inference and operations research methods
利用机器学习、因果推理和运筹学方法定制卫生政策以改善结果
- 批准号:
MR/T04487X/1 - 财政年份:2020
- 资助金额:
$ 6.27万 - 项目类别:
Research Grant
Improving statistical methods to address confounding in the economic evaluation of health interventions
改进统计方法以解决健康干预措施经济评估中的混杂问题
- 批准号:
MR/L012332/1 - 财政年份:2014
- 资助金额:
$ 6.27万 - 项目类别:
Fellowship
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- 资助金额:24.0 万元
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