Missing data in alcohol clinical trials: a comparison of methods.
Missing data in alcohol clinical trials: a comparison of methods.
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DOI：
10.1111/acer.12205
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发表时间：
2013-12
期刊：
影响因子：
--
通讯作者：
Witkiewitz K
中科院分区：
文献类型：
--
作者：
Hallgren KA;Witkiewitz K
酒精临床试验中的受试者流失率通常很高，可能会导致治疗效果统计分析中缺失数据处理方面的重大问题。研究人员通常假设缺失数据指示参与者复发，并且在该假设下，许多研究人员依赖于将所有缺失值设置为结果的最坏情况（例如，酗酒（heavy drinking）。这种单一归因方法因在临床研究的其他领域产生偏倚结果而受到批评，但尚未在酒精临床试验的背景下进行评估，许多酒精研究人员继续使用缺失=重度饮酒假设。来自联合收割机研究（一项多中心随机临床试验）的数据用于在各种条件和假设下生成缺失数据的模拟情况。我们在三种缺失数据假设（完全随机缺失、随机缺失、非随机缺失）下操纵样本量（n = 200、n = 500和n = 1000）和脱落率（5%、10%、25%、30%）。然后，我们使用五种处理缺失数据的方法（完整病例分析、末次观察值结转、缺失=重度饮酒、多重插补和全信息最大似然法），研究了治疗后前10周内接受纳洛酮和重度饮酒之间的关联。完整的病例分析，最后一次观察结转，和缺失=大量饮酒产生了最有偏见的纳洛酮效果估计和标准误的条件下，可能存在于随机临床试验。多重插补和最大似然法产生了最小偏倚的纳洛酮效应估计值和标准误。假设缺失=重度饮酒会产生治疗效果的偏倚结果，不应用于评估酒精临床试验中的治疗效果。
The rate of participant attrition in alcohol clinical trials is often substantial and can cause significant issues with regard to the handling of missing data in statistical analyses of treatment effects. It is common for researchers to assume that missing data is indicative of participant relapse and under that assumption many researchers have relied on setting all missing values to the worst case scenario for the outcome (e.g., missing=heavy drinking). This sort of single imputation method has been criticized for producing biased results in other areas of clinical research, but has not been evaluated within the context of alcohol clinical trials and many alcohol researchers continue to use the missing=heavy drinking assumption. Data from the COMBINE study, a multisite randomized clinical trial, were used to generate simulated situations of missing data under a variety of conditions and assumptions. We manipulated the sample size (n = 200, n = 500, and n = 1000) and dropout rate (5%, 10%, 25%, 30%) under three missing data assumptions (missing completely at random, missing at random, missing not at random). We then examined the association between receiving naltrexone and heavy drinking during the first 10 weeks following treatment using five methods for treating missing data (complete case analysis, last observation carried forward, missing=heavy drinking, multiple imputation, and full information maximum likelihood). Complete case analysis, last observation carried forward, and missing=heavy drinking produced the most biased naltrexone effect estimates and standard errors under conditions that are likely to exist in randomized clinical trials. Multiple imputation and maximum likelihood produced the least biased naltrexone effect estimates and standard errors. Assuming that missing=heavy drinking produces biased results of the treatment effect and should not be used to evaluate treatment effects in alcohol clinical trials.
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