Longitudinal data are very popular in practice, but they are often missing in either outcomes or time-dependent risk factors, making them highly unbalanced and complex. Missing data may contain various missing patterns or mechanisms, and how to properly handle it for unbiased and valid inference still presents a significant challenge. Here, we propose a novel semi-parametric framework for analyzing longitudinal data with both missing responses and covariates that are missing at random and intermittent, a general and widely encountered situation in observational studies. Within this framework, we consider multiple robust estimation procedures based on innovative calibrated propensity scores, which offers additional relaxation of the misspecification of missing data mechanisms and shows more satisfactory numerical performance. Also, the corresponding robust information criterion on consistent variable selection for our proposed model is developed based on empirical likelihood-based methods. These advocated methods are evaluated in both theory and extensive simulation studies in a variety of situations, showing competing properties and advantages compared to the existing approaches. We illustrate the utility of our approach by analyzing the data from the HIV Epidemiology Research Study.

中文翻译：

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一种用于间歇性缺失数据纵向分析的多重稳健倾向评分方法

纵向数据在实践中非常流行，但它们往往在结果或时间相关的风险因素中缺失，使它们高度不平衡和复杂。缺失的数据可能包含各种缺失的模式或机制，如何正确处理它以进行无偏见和有效的推理仍然是一个重大挑战。在这里，我们提出了一种新的半参数框架，用于分析具有随机和间歇性缺失响应和协变量的纵向数据，这是观察性研究中普遍且广泛遇到的情况。在这个框架内，我们考虑了基于创新的校准倾向分数的多个稳健估计程序，这提供了对缺失数据机制的错误指定的额外放松，并显示出更令人满意的数值性能。还，我们提出的模型的一致变量选择的相应稳健信息标准是基于基于经验似然的方法开发的。这些提倡的方法在各种情况下的理论和广泛的模拟研究中都得到了评估，与现有方法相比，显示出竞争特性和优势。我们通过分析来自 HIV 流行病学研究的数据来说明我们方法的实用性。