A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes.
A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes.
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DOI：
10.1186/1745-6215-9-51
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发表时间：
2008-08-11
期刊：
影响因子：
2.5
通讯作者：
Ramsay CR
中科院分区：
文献类型：
--
作者：
Fielding S;Maclennan G;Cook JA;Ramsay CR
随机对照试验（RCT）被认为是评估医疗保健干预措施的金标准方法，并且越来越多地包括生活质量（QoL）指标。如果有相当比例的结局数据缺失，则观察到的结果易受偏倚影响。该综述旨在确定是否使用插补来处理缺失的QoL结局。随机选择2005/6年发表在《英国医学杂志》、《柳叶刀》、《新英格兰医学杂志》和《美国医学会杂志》上的285篇随机对照试验。61项（21%）试验报告了QoL结局。6例（10%）报告无缺失数据，20例（33%）报告缺失≤ 10%，11例（18%）缺失11%-20%，11例（18%）缺失>20%。13例（21%）缺失不明确。61项试验中有19项（31%）对缺失数据进行了插补。在13项试验中，插补是主要分析的一部分，但在6项试验中，插补是敏感性分析的一部分。在12项试验中使用了末次值结转，在2项试验中使用了多重插补。插补后，最常见的分析方法是协方差分析（10项试验）。大多数研究没有插补缺失数据，并进行了完整的病例分析。对于那些确实估算了缺失数据的研究，研究人员倾向于更简单的估算方法，尽管有更复杂的方法可用。
Randomised controlled trials (RCTs) are perceived as the gold-standard method for evaluating healthcare interventions, and increasingly include quality of life (QoL) measures. The observed results are susceptible to bias if a substantial proportion of outcome data are missing. The review aimed to determine whether imputation was used to deal with missing QoL outcomes. A random selection of 285 RCTs published during 2005/6 in the British Medical Journal, Lancet, New England Journal of Medicine and Journal of American Medical Association were identified. QoL outcomes were reported in 61 (21%) trials. Six (10%) reported having no missing data, 20 (33%) reported ≤ 10% missing, eleven (18%) 11%–20% missing, and eleven (18%) reported >20% missing. Missingness was unclear in 13 (21%). Missing data were imputed in 19 (31%) of the 61 trials. Imputation was part of the primary analysis in 13 trials, but a sensitivity analysis in six. Last value carried forward was used in 12 trials and multiple imputation in two. Following imputation, the most common analysis method was analysis of covariance (10 trials). The majority of studies did not impute missing data and carried out a complete-case analysis. For those studies that did impute missing data, researchers tended to prefer simpler methods of imputation, despite more sophisticated methods being available.
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