Fallacies of last observation carried forward analyses.
Fallacies of last observation carried forward analyses.
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DOI：
10.1177/1740774515602688
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发表时间：
2016-04
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影响因子：
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通讯作者：
Lachin JM
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作者：
Lachin JM
末次观测值结转（LOCF）是分析纵向重复测量数据的常用统计方法，其中可能缺失一些随访观测值。在LOCF分析中，缺失的随访访视值被替换为（插补为）该受试者的既往观察值，即结转末次观察值。然后分析观察数据和插补数据的组合，就像没有缺失数据一样。有大量的统计数据证明了这种方法的错误。2012年，国家研究理事会的临床试验缺失数据处理小组发布了一份报告，对LOCF的使用提出了担忧，并描述了提供更大统计有效性的替代方法。然而，这种方法仍然存在，而且使用非常普遍。使用Google Scholar搜索关键词“LOCF”，仅在2014年就产生了“约1360”篇已发表的引文，其中绝大多数是科学研究的结果。然而，没有一个简单的解释LOCF的统计缺陷。在此给出了这样的描述。描述了一个简单的重复测量模型，用于两个时间点（例如1年和2年）的定量观察，1年时的完整值用于在完全随机缺失（MCAR）假设下通过LOCF插补2年时的缺失值。这导致2年时观察值和插补值的混合分布，平均值和方差是1年和2年分布混合的函数。表达式表明，LOCF只有在1年时观察值的分布恰好等于2年时缺失值的分布时才是无偏的，当然后者是未知的。当2年的值不是随机缺失时，如果没有额外的不可验证的假设，就不可能简单地表达混合分布的均值和方差。所有使用LOCF的分析都是有问题的，如果不是完全似是而非的话（def：看起来是真的，但实际上是假的）。我们希望未来的研究将更积极地尝试减少缺失数据的数量，并在出现缺失数据的情况下进行更有效的统计分析。LOCF不应用于任何分析。
Last Observation Carried Forward (LOCF) is a common statistical approach to the analysis of longitudinal repeated measures data where some follow-up observations may be missing. In a LOCF analysis, a missing follow-up visit value is replaced by (imputed as) that subject’s previously observed value, i.e. the last observation is carried forward. The combination of the observed and imputed data are then analyzed as though there were no missing data. There have been numerous statistical demonstrations of faults of this approach. In 2012 the National Research Council’s Panel on Handling Missing Data in Clinical Trials issued a report that raised concerns with the use of LOCF, and described alternate methods that offer greater statistical validity. Nevertheless, the method persists and its use is rampant. A search of the key word “LOCF” using Google Scholar yielded “about 1360” published citations during 2014 alone, the overwhelming majority presenting the results of scientific studies. However, there has not been a simple explanation of the statistical deficiencies of LOCF. Such a description is presented herein. A simple repeated measures model is described for quantitative observations at two times (e.g. 1 and 2-years), with complete values at 1-year that are used to impute by LOCF the missing values at 2-years under the missing completely at random (MCAR) assumption. This results in a mixture distribution of observed and imputed values at 2-years with mean and variance that are a function of the mixture of the 1 and 2-year distributions. The expressions show that LOCF is only unbiased when the distribution of the observed values at 1-year is exactly equal to the distribution of the missing values at 2-years, the latter of course being unknown. When the values at 2-years are not randomly missing, no simple expressions for the mean and variance of the mixture distribution are possible without additional unverifiable assumptions. All analyses using LOCF are of questionable veracity, if not being outright specious (def: appearing to be true but actually false). It is hoped that future studies will make a more vigorous attempt to minimize the amount of missing data, and that more valid statistical analyses will be employed in cases where missing data occurs. LOCF should not be employed in any analyses.