In this article, we propose a new method for analyzing longitudinal data which contain responses that are missing at random. This method consists in solving the generalized estimating equation (GEE) of [8] in which the incomplete responses are replaced by values adjusted using the inverse probability weights proposed in [17]. We show that the root estimator is consistent and asymptotically normal, essentially under the some conditions on the marginal distribution and the surrogate correlation matrix as those presented in [15] in the case of complete data, and under minimal assumptions on the missingness probabilities. This method is applied to a real-life data set taken from [13], which examines the incidence of respiratory disease in a sample of 250 pre-school age Indonesian children which were examined every 3 months for 18 months, using as covariates the age, gender, and vitamin A deficiency.

中文翻译：

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纵向数据的渐近理论，其缺失响应经反概率权重调整

在本文中，我们提出了一种用于分析包含随机丢失的响应的纵向数据的新方法。该方法在于求解[8]的广义估计方程（GEE），其中不完整的响应由使用[17]中提出的逆概率权重调整的值代替。我们表明，根估计量是一致且渐近正态的，基本上在边际分布和代理相关矩阵的某些条件下（如[15]中所述），在完整数据的情况下，并且在缺失概率的最小假设下。此方法应用于[13]的真实生活数据集，该数据集检查了250名学龄前印度尼西亚儿童的样本中呼吸系统疾病的发生率，每3个月检查一次，持续18个月，