Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?
Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?
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DOI：
10.1186/s13063-016-1473-3
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发表时间：
2016-07-22
期刊：
影响因子：
2.5
通讯作者：
Faragher, E. Brian
中科院分区：
文献类型：
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作者：
Mukaka, Mavuto;White, Sarah A.;Faragher, E. Brian
背景：结果缺失会严重损害从随机对照试验(RCT)中做出正确推断的能力。完全病例(CC)分析是常用的，但它减少了样本数量，并被认为导致估计的统计效率降低，同时增加了偏差的可能性。由于多重补偿(MI)方法保持了样本量，因此它们通常被视为首选的分析方法。我们检验了这一假设，比较了CC和MI方法在存在缺失二元结果的情况下确定风险差异(RD)估计的性能。结果：对于随机缺失(MAR)或完全随机缺失(MCAR)的结果，CC方法的估计总体上保持无偏，其精度与MI方法相当或更好，且具有较高的统计覆盖率。缺失非随机(Mnar)场景产生了两种方法的无效推论。在MI方法中，通过总是包括组成员来减少效应大小估计偏差，即使这与遗漏无关。令人惊讶的是，在MAR和MCAR条件下，MI没有提供比CC方法更好的统计优势。结论：虽然MI必须固有地伴随CC方法进行意向处理分析，但这些发现支持CC方法在这些条件下进行按方案的风险差异分析。这些发现为使用CC方法总是补充MI分析提供了一个论据，通常的警告是，遗漏机制的有效性应该得到彻底的讨论。更重要的是，研究人员应该努力收集尽可能多的数据。
Background: Missing outcomes can seriously impair the ability to make correct inferences from randomized controlled trials (RCTs). Complete case (CC) analysis is commonly used, but it reduces sample size and is perceived to lead to reduced statistical efficiency of estimates while increasing the potential for bias. As multiple imputation (MI) methods preserve sample size, they are generally viewed as the preferred analytical approach. We examined this assumption, comparing the performance of CC and MI methods to determine risk difference (RD) estimates in the presence of missing binary outcomes. We conducted simulation studies of 5000 simulated data sets with 50 imputations of RCTs with one primary follow-up endpoint at different underlying levels of RD (3-25 %) and missing outcomes (5-30 %).Results: For missing at random (MAR) or missing completely at random (MCAR) outcomes, CC method estimates generally remained unbiased and achieved precision similar to or better than MI methods, and high statistical coverage. Missing not at random (MNAR) scenarios yielded invalid inferences with both methods. Effect size estimate bias was reduced in MI methods by always including group membership even if this was unrelated to missingness. Surprisingly, under MAR and MCAR conditions in the assessed scenarios, MI offered no statistical advantage over CC methods.Conclusion: While MI must inherently accompany CC methods for intention-to-treat analyses, these findings endorse CC methods for per protocol risk difference analyses in these conditions. These findings provide an argument for the use of the CC approach to always complement MI analyses, with the usual caveat that the validity of the mechanism for missingness be thoroughly discussed. More importantly, researchers should strive to collect as much data as possible.