Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression.
Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression.
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DOI：
10.1093/aje/kwv114
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发表时间：
2015-10-15
影响因子：
5
通讯作者：
Carpenter JR
中科院分区：
文献类型：
--
作者：
Bartlett JW;Harel O;Carpenter JR
缺失数据是流行病学研究中经常发生的威胁有效性和效率的问题。也许处理缺失数据的最常见方法是简单地删除那些具有1个或多个缺失值的记录，即所谓的“完整记录”或“完整案例”分析。在本文中，我们汇集了早期派生的，但现在可能有点被忽视的结果表明，逻辑回归完整的记录分析可以提供渐近无偏估计的利益与结果，调整了一些混杂因素，在一个令人惊讶的范围内的缺失数据的假设下的关联的曝光。我们给出了详细的指导，描述了如何使用观测数据来判断这些假设的可行性。这些结果意味着，在受缺失数据影响并通过逻辑回归分析的大型流行病学研究中，在研究人员可能假设会发生偏倚的许多情况下，可以估计暴露相关性，而不会产生偏倚。
Missing data are a commonly occurring threat to the validity and efficiency of epidemiologic studies. Perhaps the most common approach to handling missing data is to simply drop those records with 1 or more missing values, in so-called “complete records” or “complete case” analysis. In this paper, we bring together earlier-derived yet perhaps now somewhat neglected results which show that a logistic regression complete records analysis can provide asymptotically unbiased estimates of the association of an exposure of interest with an outcome, adjusted for a number of confounders, under a surprisingly wide range of missing-data assumptions. We give detailed guidance describing how the observed data can be used to judge the plausibility of these assumptions. The results mean that in large epidemiologic studies which are affected by missing data and analyzed by logistic regression, exposure associations may be estimated without bias in a number of settings where researchers might otherwise assume that bias would occur.