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A Bayesian framework to handle missing data in cost-effectiveness analysis (CEA) alongside with randomised longitudinal trials

用于处理成本效益分析 (CEA) 中缺失数据以及随机纵向试验的贝叶斯框架

基本信息

批准号：
2248659
负责人：
金额：
--
依托单位：
University College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2248659
关键词：
Bayesian framework handle missing data

项目摘要

My PhD aims to develop a semi-parametric Bayesian framework to handle missing data in economic evaluations alongside longitudinal studies and randomised trials. Innovation and research in health technology have achieved remarkable progress over the past decades. While the technological breakthroughs in health care have largely improved patients' clinical outcomes and quality of life, they pose additional economic burden to countries with limited budgets. Thus it has generated wide concerns about how to scientifically assess the value of health technologies and choose between them based on their broader impact on users, payers, policy decision-makers and even the whole society. Economic evaluation, in particular, cost-effectiveness analysis (CEA), has become one of the major tools in health technology assessment. The method usually incorporates all available and appropriate evidence collected from trials through a follow-up period, and formerly compares the costs and health benefits of existing and new medical technologies and public health programs to inform the decision about the coverage or reimbursement of public health services (Briggs et al., 2006). The longitudinal nature of data often encountered in CEA makes it prone to the threat of non-responses which may lead to biased results and the waste of limited resources if missing data could not be appropriately handled.Although standard statistical tools such as multiple imputation (MI) have been well developed to deal with missingness and can produce valid estimates if they can be performed properly, their extensions in CEA have been hindered due to the increasing complexity in the modelling. Examples of this complexity include the necessity of considering a bivariate outcome comprising costs and effectiveness data, as well as specific features such as e.g., skewness, spikes and correlation between observations. More importantly, the underlying decision uncertainty brought by plausible missingness assumptions also needs careful consideration (Gabrio et al., 2018b). These features make Bayesian modelling a preferable alternative in this context. Moreover, Bayesian modelling can bring external evidence such as expert opinions to decision making through prior distributions and this benefit is crucial in economic evaluation alongside longitudinal studies as missing values are more likely to be non-ignorable. Recent research has explored the substantial advantages to perform economic evaluations under a parametric Bayesian framework - the capacity and flexibility to allow the complexity above, assess the robustness of missingness assumptions and avoid potential risks of traditional methods (Gabrio et al., 2018a, Gabrio et al., 2018b). However, models with more relaxing assumptions can be exploited to handle more realistic situations. My PhD project will further this field by developing a semi-parametric model for missingness in economic evaluations with Bayesian nonparametric modelling and formally comparing it with existing models to see if our model has the potential to work as the standard tool in health technology assessment.BRIGGS, A., SCULPHER, M. & CLAXTON, K. 2006. Decision modelling for health economic evaluation, OUP Oxford.GABRIO, A., DANIELS, M. J. & BAIO, G. 2018a. A Bayesian Parametric Approach to Handle Missing Longitudinal Outcome Data in Trial-Based Health Economic Evaluations. arXiv preprint arXiv:1805.07147.GABRIO, A., MASON, A. J. & BAIO, G. 2018b. A full Bayesian model to handle structural ones and missingness in economic evaluations from individual-level data. Statistics in Medicine.

我的博士学位旨在开发一个半参数贝叶斯框架，以处理经济评估中的缺失数据以及纵向研究和随机试验。过去几十年来，卫生技术的创新和研究取得了显著进展。虽然医疗保健领域的技术突破在很大程度上改善了患者的临床结果和生活质量，但它们给预算有限的国家带来了额外的经济负担。因此，如何科学地评估卫生技术的价值，并根据其对使用者、支付者、政策决策者甚至整个社会的广泛影响在这些技术之间进行选择，这引起了广泛的关注。经济学评价，特别是成本效果分析（CEA），已成为卫生技术评价的主要工具之一。该方法通常包括从试验中收集的所有可用和适当的证据，通过随访期，以前比较现有和新的医疗技术和公共卫生计划的成本和健康效益，以告知有关公共卫生服务覆盖或报销的决定（Briggs et al.，2006年）。CEA中经常遇到的数据的纵向性质使其容易受到无应答的威胁，这可能导致有偏倚的结果，并且如果缺失数据不能得到适当的处理，则会浪费有限的资源。虽然标准的统计工具（如多重插补（MI））已经很好地开发用于处理缺失，并且如果能够正确执行，可以产生有效的估计，由于建模的复杂性增加，它们在CEA中的扩展受到阻碍。这种复杂性的例子包括考虑包括成本和有效性数据的双变量结果的必要性，以及具体特征，例如，偏度、峰值和观测值之间的相关性。更重要的是，由合理的缺失假设带来的潜在决策不确定性也需要仔细考虑（Gabrio等人，2018年b）。这些特点使贝叶斯建模在这方面的一个更可取的选择。此外，贝叶斯模型可以通过先验分布为决策提供外部证据，如专家意见，这一好处在经济评估以及纵向研究中至关重要，因为缺失值更有可能是不可验证的。最近的研究已经探索了在参数贝叶斯框架下进行经济评估的实质性优势-允许上述复杂性的能力和灵活性，评估缺失假设的鲁棒性并避免传统方法的潜在风险（Gabrio等人，2018 a，Gabrio等人，2018年b）。然而，可以利用具有更宽松假设的模型来处理更现实的情况。我的博士项目将进一步通过开发一个半参数模型与贝叶斯非参数模型在经济评估中的缺失，并正式将其与现有模型进行比较，看看我们的模型是否有潜力作为卫生技术评估的标准工具。斯考尔弗&克拉克斯顿，K. 2006.卫生经济评价决策模型，牛津大学出版社，加布里奥，A.，丹尼尔斯J. & BAIO，G. 2018年a。贝叶斯参数方法处理试验为基础的卫生经济学评价中缺失的纵向结果数据。arXiv预印本arXiv：1805.07147。梅森，A. J. & BAIO，G. 2018年b。一个完整的贝叶斯模型，用于处理个人层面数据的经济评估中的结构性和缺失。医学统计。