Handling Missing Data in Patient-Level Cost-Effectiveness Analysis alongside Randomised Clinical Trials
Handling Missing Data in Patient-Level Cost-Effectiveness Analysis alongside Randomised Clinical Trials
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DOI：
10.2165/00148365-200504020-00001
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发表时间：
2005-06-01
影响因子：
3.6
通讯作者：
Palmer, Stephen
中科院分区：
文献类型：
--
作者：
Manca, Andrea;Palmer, Stephen
背景：在与随机临床试验同时进行的成本效果分析中，数据缺失可能是一个广泛的问题，在随机临床试验中，需要对资源使用和健康结果信息进行前瞻性收集。存在不完整记录有几个可能的原因，在存在缺失值的数据的情况下进行分析的有效性取决于产生缺失数据现象的机制。过去，用于分析观察不完整的数据集的最常用方法是相对特别的(例如，病例删除、均值推算)，并受到潜在限制。最近，人们提出了几种替代的更复杂的方法(如多重推算)，试图纠正简单推算方法的缺陷。目标：目的是对基于试验的成本-效果分析中最常用的处理缺失数据的定量方法进行简明和易懂的描述，并展示这些替代方法对两个案例研究报告的成本-效果结果的潜在影响。方法：使用最近进行的两项基于试验的经济评估的数据来探讨研究结果对用于处理不完全观察的技术的敏感性。使用基于净收益和成本-效果可接受曲线的方法，概述了表示替代方法中的不确定性的统计框架。结果：案例研究证明了用于处理缺失数据的方法的潜在重要性。虽然分析策略似乎不会改变其中一个研究的结果，但另一个案例研究表明，成本-效果分析的结果对归因决定和所采用的归因策略都很敏感。结论：在存在缺失数据的情况下，分析人员应该更明确地报告应用的分析策略。在大多数情况下，建议使用多重推算方法，以便充分反映由于存在缺失数据而导致的研究结果的不确定性。
Background: Missing data are potentially an extensive problem in cost-effectiveness analyses conducted alongside randomised clinical trials, where prospective collection of both resource use and health outcome information is required. There are several possible reasons for the presence of incomplete records, and the validity of the analysis in the presence of data with missing values is dependent upon the mechanism generating the missing data phenomenon. In the past, the most commonly used methods for analysing datasets with incomplete observations were relatively ad hoc (e.g. case deletion, mean imputation) and suffered from potential limitations. Recently, several alternative and more sophisticated approaches (e.g. multiple imputation) have been proposed that attempt to correct the flaws of the simple imputation methods.Objectives: The objectives are to provide a concise and accessible description of the quantitative methods most commonly used in trial-based cost-effectiveness analysis for handling missing data, and also to demonstrate the potential impact of these alternative approaches on the cost-effectiveness results reported in two case studies. Methods: Data from two recently conducted, trial-based economic evaluations are used to explore the sensitivity of the study results to the technique used to deal with incomplete observations. A statistical framework for representing the uncertainty in the alternative methods is outlined using an approach based on net benefits and cost-effectiveness acceptability curves.Results: The case studies demonstrate the potential importance of the approach used to handle missing data. Although the analytical strategy did not appear to alter the results of one of the studies, the other case study showed that that the results of the cost-effectiveness analysis were sensitive to both the decision to impute and also the imputation strategy adopted.Conclusions: Analysts should be more explicit in reporting the analytical strategies applied in the presence of missing data. The use of a multiple imputation approach is recommended in the majority of cases, so as to adequately reflect the uncertainty in the study results due to the presence of missing data.