Lu Z Kevin, Xiong Xiaomo, Lee Taiying, Wu Jun, Yuan Jing, Jiang Bin
Department of Clinical Pharmacy and Outcomes Sciences, University of South Carolina, Columbia, SC, United States.
Department of Pharmaceutical and Administrative Sciences, Presbyterian College School of Pharmacy, Clinton, SC, United States.
Front Pharmacol. 2021 Oct 19;12:700012. doi: 10.3389/fphar.2021.700012. eCollection 2021.
Big data and real-world data (RWD) have been increasingly used to measure the effectiveness and costs in cost-effectiveness analysis (CEA). However, the characteristics and methodologies of CEA based on big data and RWD remain unknown. The objectives of this study were to review the characteristics and methodologies of the CEA studies based on big data and RWD and to compare the characteristics and methodologies between the CEA studies with or without decision-analytic models. The literature search was conducted in Medline (Pubmed), Embase, Web of Science, and Cochrane Library (as of June 2020). Full CEA studies with an incremental analysis that used big data and RWD for both effectiveness and costs written in English were included. There were no restrictions regarding publication date. 70 studies on CEA using RWD (37 with decision-analytic models and 33 without) were included. The majority of the studies were published between 2011 and 2020, and the number of CEA based on RWD has been increasing over the years. Few CEA studies used big data. Pharmacological interventions were the most frequently studied intervention, and they were more frequently evaluated by the studies without decision-analytic models, while those with the model focused on treatment regimen. Compared to CEA studies using decision-analytic models, both effectiveness and costs of those using the model were more likely to be obtained from literature review. All the studies using decision-analytic models included sensitivity analyses, while four studies no using the model neither used sensitivity analysis nor controlled for confounders. The review shows that RWD has been increasingly applied in conducting the cost-effectiveness analysis. However, few CEA studies are based on big data. In future CEA studies using big data and RWD, it is encouraged to control confounders and to discount in long-term research when decision-analytic models are not used.
大数据和真实世界数据（RWD）在成本效益分析（CEA）中越来越多地用于衡量有效性和成本。然而，基于大数据和真实世界数据的CEA的特征和方法仍然未知。本研究的目的是回顾基于大数据和真实世界数据的CEA研究的特征和方法，并比较使用或不使用决策分析模型的CEA研究之间的特征和方法。在Medline（PubMed）、Embase、科学网和Cochrane图书馆（截至2020年6月）进行文献检索。纳入了使用大数据和真实世界数据进行有效性和成本的增量分析的完整CEA研究，英文撰写。对出版日期没有限制。纳入了70项使用真实世界数据的CEA研究（37项使用决策分析模型，33项未使用）。大多数研究发表于2011年至2020年之间，多年来基于真实世界数据的CEA数量一直在增加。很少有CEA研究使用大数据。药物干预是研究最频繁的干预措施，未使用决策分析模型的研究对其评估更为频繁，而使用模型的研究则侧重于治疗方案。与使用决策分析模型的CEA研究相比，使用该模型的研究的有效性和成本更有可能从文献综述中获得。所有使用决策分析模型的研究都包括敏感性分析，而四项未使用该模型的研究既未使用敏感性分析也未控制混杂因素。该综述表明，真实世界数据在进行成本效益分析中越来越多地得到应用。然而，很少有CEA研究基于大数据。在未来使用大数据和真实世界数据的CEA研究中，鼓励在不使用决策分析模型时控制混杂因素，并在长期研究中进行贴现。
