Dealing With Missing Outcome Data in Randomized Trials and Observational Studies
Dealing With Missing Outcome Data in Randomized Trials and Observational Studies
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DOI：
10.1093/aje/kwr302
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发表时间：
2012-02-01
影响因子：
5
通讯作者：
Moons, Karel G. M.
中科院分区：
文献类型：
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作者：
Groenwold, Rolf H. H.;Donders, A. Rogier T.;Moons, Karel G. M.
虽然丢失结果数据是随机试验和观察性研究中的一个重要问题，但解决这个问题的方法可能很难应用。使用模拟数据，作者比较了3种处理缺失结果数据的方法：1)完全病例分析；2)单一归因法；3)多重归因法(所有3种方法都有或没有协变量调整)。模拟情景集中于来自随机试验或观察性研究的连续或二分缺失结果数据。当结果随机缺失时，在协变量调整后，单次和多次推算得到的估计是无偏的。通过协变量调整的完整病例分析获得的估计也是无偏见的，覆盖率接近95%。当结果数据不是随机丢失时，所有方法都给出了有偏的估计，但与没有协变量调整的完整病例分析相比，用3种方法中的1种方法处理丢失的结果数据减少了偏差。协变量调整和多重归因的完整病例分析在丢失结果数据的情况下产生类似的估计，只要包括相同的丢失预测因素。因此，协变量调整后的完整病例分析可以而且应该更多地被用作选择分析。此外，多重归因可以更灵活地适应丢失而不是随机的情况，使其特别适合于敏感性分析。
Although missing outcome data are an important problem in randomized trials and observational studies, methods to address this issue can be difficult to apply. Using simulated data, the authors compared 3 methods to handle missing outcome data: 1) complete case analysis; 2) single imputation; and 3) multiple imputation (all 3 with and without covariate adjustment). Simulated scenarios focused on continuous or dichotomous missing outcome data from randomized trials or observational studies. When outcomes were missing at random, single and multiple imputations yielded unbiased estimates after covariate adjustment. Estimates obtained by complete case analysis with covariate adjustment were unbiased as well, with coverage close to 95%. When outcome data were missing not at random, all methods gave biased estimates, but handling missing outcome data by means of 1 of the 3 methods reduced bias compared with a complete case analysis without covariate adjustment. Complete case analysis with covariate adjustment and multiple imputation yield similar estimates in the event of missing outcome data, as long as the same predictors of missingness are included. Hence, complete case analysis with covariate adjustment can and should be used as the analysis of choice more often. Multiple imputation, in addition, can accommodate the missing-not-at-random scenario more flexibly, making it especially suited for sensitivity analyses.