Doubly Robust and Multiple-Imputation-Based Generalized Estimating Equations
Doubly Robust and Multiple-Imputation-Based Generalized Estimating Equations
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DOI：
10.1080/10543406.2011.550096
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发表时间：
2011-01-01
影响因子：
1.1
通讯作者：
Kenward, Michael G.
中科院分区：
文献类型：
--
作者：
Birhanu, Teshome;Molenberghs, Geert;Kenward, Michael G.
广义估计方程（GEE）是Liang和Zeger（1986）提出的一种分析相关非高斯数据的方法。当数据是不完整的，GEE方法遭受其频率论的性质和推理下，这种方法是有效的，只有在很强的假设下，缺失的数据是完全随机缺失。当响应数据随机缺失时，可以考虑基于逆概率加权或多重插补对GEE进行两种修改。加权GEE（WGEE）方法涉及通过观察到的概率的倒数来加权观察。插补方法涉及用假定插补模型预测的值多次填充缺失观测值。所谓的双重鲁棒（DR）方法涉及权重模型和给定观测值的缺失观测值的预测模型。为了得到一致的估计值，WGEE需要正确指定脱落模型，而基于插补的方法需要正确指定插补模型。DR方法需要正确指定权重或预测模型，但不一定两者都需要。专注于不完整的二进制重复测量，我们研究了相对性能的单鲁棒和双鲁棒版本的GEE在各种正确和不正确的指定模型，使用模拟研究。甲真菌病的临床试验数据进一步说明了这种方法。
Generalized estimating equations (GEE), proposed by Liang and Zeger (1986), provide a popular method to analyze correlated non-Gaussian data. When data are incomplete, the GEE method suffers from its frequentist nature and inferences under this method are valid only under the strong assumption that the missing data are missing completely at random. When response data are missing at random, two modifications of GEE can be considered, based on inverse-probability weighting or on multiple imputation. The weighted GEE (WGEE) method involves weighting observations by the inverse of their probability of being observed. Imputation methods involve filling in missing observations with values predicted by an assumed imputation model, multiple times. The so-called doubly robust (DR) methods involve both a model for the weights and a predictive model for the missing observations given the observed ones. To yield consistent estimates, WGEE needs correct specification of the dropout model while imputation-based methodology needs a correctly specified imputation model. DR methods need correct specification of either the weight or the predictive model, but not necessarily both. Focusing on incomplete binary repeated measures, we study the relative performance of the singly robust and doubly robust versions of GEE in a variety of correctly and incorrectly specified models using simulation studies. Data from a clinical trial in onychomycosis further illustrate the method.