Bias correction for nonignorable missing counts of areal HIV new diagnosis
Bias correction for nonignorable missing counts of areal HIV new diagnosis
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DOI：
10.1002/sta4.555
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发表时间：
2023-01-01
期刊：
影响因子：
1.7
通讯作者：
Albarracin，Dolores
中科院分区：
文献类型：
--
作者：
Qu，Tianyi;Li，Bo;Albarracin，Dolores
公共卫生数据，如艾滋病毒的新诊断，往往由于保密问题而被保留审查。标准分析方法假设删失值为随机缺失，通常会导致有偏估计和较差的预测。受费城HIV新诊断的面积计数的启发，所有小于或等于5的值都被抑制，我们提出了两种方法来减少遗漏对预测和面积HIV新诊断的插补的不利影响。一种是将缺失机制集成到似然函数中的基于似然的方法，另一种是用于矩阵因子分解插补的非参数算法。数值研究和Philadelphia数据分析表明，所提出的两种方法可以显着改善基于左删失HIV数据的预测和插补。我们还比较了这两种方法对模型误设定的鲁棒性，发现这两种方法对预测都是鲁棒的，而它们的插补性能取决于模型规格。
Public health data, such as HIV new diagnoses, are often left‐censored due to confidentiality issues. Standard analysis approaches that assume censored values as missing at random often lead to biased estimates and inferior predictions. Motivated by the Philadelphia areal counts of HIV new diagnosis for which all values less than or equal to 5 are suppressed, we propose two methods to reduce the adverse influence of missingness on predictions and imputation of areal HIV new diagnoses. One is the likelihood‐based method that integrates the missing mechanism into the likelihood function, and the other is a nonparametric algorithm for matrix factorization imputation. Numerical studies and the Philadelphia data analysis demonstrate that the two proposed methods can significantly improve prediction and imputation based on left‐censored HIV data. We also compare the two methods on their robustness to model misspecification and find that both methods appear to be robust for prediction, while their performance for imputation depends on model specification.