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首页> 外文期刊>Psychometrika >Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes
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Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes

机译：使用带有非单调缺少纵向二元成果的GEE的多重估算

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This paper considers multiple imputation (MI) approaches for handling non-monotone missing longitudinal binary responses when estimating parameters of a marginal model using generalized estimating equations (GEE). GEE has been shown to yield consistent estimates of the regression parameters for a marginal model when data are missing completely at random (MCAR). However, when data are missing at random (MAR), the GEE estimates may not be consistent; the MI approaches proposed in this paper minimize bias under MAR. The first MI approach proposed is based on a multivariate normal distribution, but with the addition of pairwise products among the binary outcomes to the multivariate normal vector. Even though the multivariate normal does not impute 0 or 1 values for the missing binary responses, as discussed by Horton et al. (Am Stat 57:229-232, 2003), we suggest not rounding when filling in the missing binary data because it could increase bias. The second MI approach considered is the fully conditional specification (FCS) approach. In this approach, we specify a logistic regression model for each outcome given the outcomes at other time points and the covariates. Typically, one would only include main effects of the outcome at the other times as predictors in the FCS approach, but we explore if bias can be reduced by also including pairwise interactions of the outcomes at other time point in the FCS. In a study of asymptotic bias with non-monotone missing data, the proposed MI approaches are also compared to GEE without imputation. Finally, the proposed methods are illustrated using data from a longitudinal clinical trial comparing four psychosocial treatments from the National Institute on Drug Abuse Collaborative Cocaine Treatment Study, where patients' cocaine use is collected monthly for 6 months during treatment.
机译：本文考虑了在使用广义估计方程（GEE）估计边际模型参数时，处理非单调缺失纵向二元响应的多重插补（MI）方法。当数据完全随机缺失（MCAR）时，GEE已被证明能对边际模型的回归参数产生一致的估计。然而，当数据随机缺失（MAR）时，GEE估计值可能不一致；本文提出的MI方法最小化了MAR下的偏差。提出的第一种MI方法基于多元正态分布，但在多元正态向量的二元结果中加入了两两乘积。尽管正如Horton等人（美国统计局57:229-232，2003）所讨论的那样，多元正态分布不会为缺失的二元响应计算0或1的值，但我们建议在填充缺失的二元数据时不要舍入，因为这可能会增加偏差。考虑的第二种MI方法是完全条件规范（FCS）方法。在这种方法中，考虑到其他时间点的结果和协变量，我们为每个结果指定了一个逻辑回归模型。通常，在FCS方法中，一种方法只会将其他时间的结果的主要影响作为预测因素，但我们探讨是否可以通过在FCS的其他时间点也包括结果的成对交互来减少偏差。在一项非单调缺失数据的渐近偏差研究中，我们还将提出的MI方法与无插补的GEE方法进行了比较。最后，通过一项纵向临床试验的数据，对美国国家药物滥用研究所（National Institute on Drug Crusion Collaborative Cokine Treatment Study）的四种心理社会治疗方法进行比较，说明了所提出的方法。在这项研究中，患者在治疗期间6个月内每月收集一次可卡因使用情况。

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