Department of Probability and Statistics, Peking University, Beijing, China.
Department of Computer Science, Johns Hopkins University, Baltimore, USA.
Biometrics. 2023 Dec;79(4):3203-3214. doi: 10.1111/biom.13916. Epub 2023 Jul 24.
We introduce an itemwise modeling approach called "self-censoring" for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome can be affected by its own value and associated with missingness indicators of other outcomes, while conditionally independent of the other outcomes. The self-censoring model complements previous graphical approaches for the analysis of multivariate nonignorable missing data. It is identified under a completeness condition stating that any variability in one outcome can be captured by variability in the other outcomes among complete cases. For estimation, we propose a suite of semiparametric estimators including doubly robust estimators that deliver valid inferences under partial misspecification of the full-data distribution. We also provide a novel and flexible global sensitivity analysis procedure anchored at the self-censoring. We evaluate the performance of the proposed methods with simulations and apply them to analyze a study about the effect of highly active antiretroviral therapy on preterm delivery of HIV-positive mothers.
我们介绍了一种称为“自我审查”的项目级建模方法，用于处理多变量不可忽略的非单调缺失数据，其中每个结果的缺失过程可以受到自身值的影响，并与其他结果的缺失指标相关联，同时与其他结果条件独立。自我审查模型补充了之前用于分析多变量不可忽略缺失数据的图形方法。它是在一个完备性条件下确定的，该条件指出，一个结果中的任何变化都可以通过完整案例中其他结果的变化来捕捉。对于估计，我们提出了一套半参数估计器，包括双稳健估计器，在全数据分布的部分指定错误的情况下提供有效的推断。我们还提供了一种新的灵活的全局敏感性分析程序，以自我审查为基础。我们通过模拟评估了所提出方法的性能，并将其应用于分析一项关于高效抗逆转录病毒疗法对 HIV 阳性母亲早产影响的研究。
