Handling drop-out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches
Handling drop-out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches
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DOI：
10.1002/pst.267
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发表时间：
2008-04-01
影响因子：
1.5
通讯作者：
Lane, Peter
中科院分区：
文献类型：
--
作者：
Lane, Peter
本研究比较了两种处理纵向试验中缺失数据的方法：一种使用末次观测值结转（LOCF）方法，另一种基于多变量或混合重复测量模型（MMRM）。使用模拟的数据集，以匹配六个实际的试验，我施加了几个脱落机制，并比较了治疗差异和治疗比较的功率方面的偏差的方法。由于活性药物组和安慰剂组的脱落率相等，LOCF通常低估了治疗效果;但由于脱落率不等，偏倚可能更大，并且在任何方向上。相比之下，MMRM方法的偏倚要小得多;而MMRM很少引起大于20%的功效差异，LOCF在近一半的模拟中引起大于20%的功效差异。因此，使用LOCF方法可能会严重歪曲试验结果，因此不是主要分析的良好选择。相比之下，MMRM方法不太可能导致严重的误解，除非脱落机制是非随机缺失（MNAR），并且存在实质上不相等的脱落。此外，MMRM显然更可靠，更有统计基础。这两种方法都不能单独处理涉及MNAR脱落机制的试验，需要使用更复杂的方法进行敏感性分析。版权所有（C）2007约翰威利父子有限公司
This study compares two methods for handling missing data in longitudinal trials: one using the last-observation-carried-forward (LOCF) method and one based on a multivariate or mixed model for repeated measurements (MMRM). Using data sets simulated to match six actual trials, I imposed several drop-out mechanisms, and compared the methods in terms of bias in the treatment difference and power of the treatment comparison. With equal drop-out in Active and Placebo arms, LOCF generally underestimated the treatment effect; but with unequal drop-out, bias could be much larger and in either direction. In contrast, bias with the MMRM method was much smaller; and whereas MMRM rarely caused a difference in power of greater than 20%, LOCF caused a difference in power of greater than 20% in nearly half the simulations. Use of the LOCF method is therefore likely to misrepresent the results of a trial seriously, and so is not a good choice for primary analysis. In contrast, the MMRM method is unlikely to result in serious misinterpretation, unless the drop-out mechanism is missing not at random (MNAR) and there is substantially unequal drop-out. Moreover, MMRM is clearly more reliable and better grounded statistically. Neither method is capable of dealing on its own with trials involving MNAR drop-out mechanisms,for which sensitivity analysis is needed using more complex methods. Copyright (C) 2007 John Wiley & Sons, Ltd.