Bias due to missing exposure data using complete-case analysis in the proportional hazards regression model
Bias due to missing exposure data using complete-case analysis in the proportional hazards regression model
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DOI：
10.1002/sim.1340
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发表时间：
2003-02-28
影响因子：
2
通讯作者：
Cupples, LA
中科院分区：
文献类型：
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作者：
Demissie, S;LaValley, MP;Cupples, LA
我们研究了使用完整案例分析（CCA）时比例风险回归模型中由于缺失暴露数据而导致的偏差。考虑了11种缺失数据场景：1种完全随机缺失（MCAR）、4种随机缺失（MAR）和6种不可忽略的缺失场景，具有各种风险比、截尾分数、缺失分数和样本量。当缺失为MCAR或仅取决于暴露量时，偏倚可忽略不计（2- 3%），与无缺失数据的完整数据集中的估计值与真实参数之间的差异相似。相比之下，当缺失取决于结果或结果和暴露时，会发生实质性偏倚。对于风险比为3.5、样本量为400、20%删失和40%缺失数据的模型，风险比的相对偏倚范围在7%至64%之间。例如，在缺失与较长或较短随访相关的情况下，偏倚明显不同，尽管两种机制均为MAR。当缺失与较长随访相关时，风险比被低估（偏倚较大），而当缺失与较短随访相关时，风险比被高估（偏倚较小）。如果已知缺失与较少观察到的结局相关，或与结局和暴露相关，则CCA可能导致无效推断，应考虑其他处理缺失数据的方法。版权所有（C）2003约翰威利父子有限公司。
We studied bias due to missing exposure data in the proportional hazards regression model when using complete-case analysis (CCA). Eleven missing data scenarios were considered: one with missing completely at random (MCAR), four missing at random (MAR), and six non-ignorable missingness scenarios, with a variety of hazard ratios, censoring fractions, missingness fractions and sample sizes. When missingness was MCAR or dependent only on the exposure, there was negligible bias (2-3 per cent) that was similar to the difference between the estimate in the full data set with no missing data and the true parameter. In contrast, substantial bias occurred when missingness was dependent on outcome or both outcome and exposure. For models with hazard ratio of 3.5, a sample size of 400, 20 per cent censoring and 40 per cent missing data, the relative bias for the hazard ratio ranged between 7 per cent and 64 per cent. We observed important differences in the direction and magnitude of biases under the various missing data mechanisms. For example, in scenarios where missingness was associated with longer or shorter follow-up, the biases were notably different, although both mechanisms are MAR. The hazard ratio was underestimated (with larger bias) when missingness was associated with longer follow-up and overestimated (with smaller bias) when associated with shorter follow-up. If it is known that missingness is associated with a less frequently observed outcome or with both the outcome and exposure, CCA may result in an invalid inference and other methods for handling missing data should be considered. Copyright (C) 2003 John Wiley Sons, Ltd.