论文标题
多元不可签名的非单极管缺失数据的自审查模型
A self-censoring model for multivariate nonignorable nonmonotone missing data
论文作者
论文摘要
我们引入了一个自我审查模型，用于多元不可签名的非单调丢失数据，其中每个结果的丢失过程都受其自身价值的影响，并且与其他结果的缺失指标有关，而有条件地与其他结果无关。自审查模型补充了以前的图形方法，用于分析多元不可签名的丢失数据。它在完整性条件下确定，表明一个结果的任何可变性都可以通过完整案例中其他结果的可变性来捕获。为了进行估计，我们提出了一套半参数估计器，包括双重稳健的估计器，这些估计值在全数据分布的部分错误指定下提供有效的推论。我们通过模拟评估了提出的估计量的性能，并将其应用于研究高度活性抗逆转录病毒治疗对HIV阳性母亲早产的影响的研究。
We introduce a self-censoring model for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome is affected by its own value and is 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 evaluate the performance of the proposed estimators with simulations and apply them to analyze a study about the effect of highly active antiretroviral therapy on preterm delivery of HIV-positive mothers.
