Philadelphia College of Osteopathic Medicine, Suwanee, GA, USA.
Abbvie Ltd., Vanwall Business Park, Maidenhead, UK.
Pharmacoeconomics. 2023 Dec;41(12):1589-1601. doi: 10.1007/s40273-023-01297-0. Epub 2023 Jul 25.
Missing data in costs and/or health outcomes and in confounding variables can create bias in the inference of health economics and outcomes research studies, which in turn can lead to inappropriate policies. Most of the literature focuses on handling missing data in randomized controlled trials, which are not necessarily always the data used in health economics and outcomes research.
We aimed to provide an overview on missing data issues and how to address incomplete data and report the findings of a systematic literature review of methods used to deal with missing data in health economics and outcomes research studies that focused on cost, utility, and patient-reported outcomes.
A systematic search of papers published in English language until the end of the year 2020 was carried out in PubMed. Studies using statistical methods to handle missing data for analyses of cost, utility, or patient-reported outcome data were included, as were reviews and guidance papers on handling missing data for those outcomes. The data extraction was conducted with a focus on the context of the study, the type of missing data, and the methods used to tackle missing data.
From 1433 identified records, 40 papers were included. Thirteen studies were economic evaluations. Thirty studies used multiple imputation with 17 studies using multiple imputation by chained equation, while 15 studies used a complete-case analysis. Seventeen studies addressed missing cost data and 23 studies dealt with missing outcome data. Eleven studies reported a single method while 20 studies used multiple methods to address missing data.
Several health economics and outcomes research studies did not offer a justification of their approach of handling missing data and some used only a single method without a sensitivity analysis. This systematic literature review highlights the importance of considering the missingness mechanism and including sensitivity analyses when planning, analyzing, and reporting health economics and outcomes research studies.
在成本和/或健康结果以及混杂变量中缺失数据会导致健康经济学和结果研究推断出现偏差，进而导致不当的政策。大多数文献都集中在处理随机对照试验中的缺失数据上，而这些数据并不一定总是健康经济学和结果研究中使用的数据。
我们旨在提供缺失数据问题的概述，以及如何处理不完整数据，并报告一项系统文献综述的结果，该综述重点关注处理成本、效用和患者报告结局的健康经济学和结果研究中缺失数据的方法。
在 PubMed 中进行了一项截至 2020 年底以英文发表的论文的系统搜索。纳入了使用统计方法处理缺失数据以分析成本、效用或患者报告结局数据的研究，以及针对这些结局处理缺失数据的综述和指导文件。数据提取的重点是研究背景、缺失数据的类型以及用于处理缺失数据的方法。
从 1433 条记录中，有 40 篇论文被纳入。其中 13 项为经济评估研究。30 项研究使用了多重插补法，其中 17 项研究使用了链方程多重插补法，15 项研究使用了完整病例分析。17 项研究处理了缺失成本数据，23 项研究处理了缺失结局数据。11 项研究报告了单一方法，20 项研究使用了多种方法来处理缺失数据。
一些健康经济学和结果研究没有对处理缺失数据的方法进行论证，有些研究只使用了单一方法而没有进行敏感性分析。这项系统文献综述强调了在规划、分析和报告健康经济学和结果研究时，考虑缺失机制和包含敏感性分析的重要性。
