Semiparametric approach for non-monotone missing covariates in a parametric regression model.
Semiparametric approach for non-monotone missing covariates in a parametric regression model.
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作者：
Sinha S;Saha KK;Wang S
在生物医学研究中，经常会出现协变量数据缺失的情况，而对这些数据的分析忽略了信息不完全的受试者，可能会导致低效和可能存在偏见的估计。当缺失机制随机缺失时，对于单个缺失协变量或缺失数据的单调模式的处理已经引起了极大的关注。本文提出了一种处理非单调缺失数据模式的半参数方法。所提出的方法依赖于这样的假设：变量的缺失机制不依赖于缺失变量本身，而可能依赖于其他缺失变量。这种机制不像完全不可忽略机制那么普遍，但有时比随机丢失机制更灵活，在随机机制中，丢失机制只允许依赖于完全观察到的变量。该方法对缺失协变量分布的错误指定具有较强的鲁棒性，并且有助于消除(或减少)不可忽略的缺失机制所导致的不可辨识性问题。给出了该估计量的渐近性质。通过仿真研究对有限样本的性能进行了评估。最后，为了说明的目的，我们分析了子宫内膜癌数据集和髋部骨折数据集。
Missing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random. In this paper, we propose a semiparametric method for handling non-monotone patterns of missing data. The proposed method relies on the assumption that the missingness mechanism of a variable does not depend on the missing variable itself but may depend on the other missing variables. This mechanism is somewhat less general than the completely non-ignorable mechanism but is sometimes more flexible than the missing at random mechanism where the missingness mechansim is allowed to depend only on the completely observed variables. The proposed approach is robust to misspecification of the distribution of the missing covariates, and the proposed mechanism helps to nullify (or reduce) the problems due to non-identifiability that result from the non-ignorable missingness mechanism. The asymptotic properties of the proposed estimator are derived. Finite sample performance is assessed through simulation studies. Finally, for the purpose of illustration we analyze an endometrial cancer dataset and a hip fracture dataset.
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DOI：
10.1198/016214504000001727
发表时间：
2004-12-01
影响因子：
3.7
作者：
Chen, HY
通讯作者：
Chen, HY
影响因子：
1.9
作者：
Ibrahim, JG;Chen, MH;Lipsitz, SR
通讯作者：
Lipsitz, SR
影响因子：
2
作者：
Kolenikov, Stanislav;Angeles, Gustavo
通讯作者：
Angeles, Gustavo
DOI：
10.2307/3316075
发表时间：
2001-06-01
影响因子：
0.6
作者：
Lei, SYC;Wang, SJ
通讯作者：
Wang, SJ
影响因子：
3.7
作者：
Lipsitz, SR;Ibrahim, JG;Zhao, LP
通讯作者：
Zhao, LP