Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Stat Med. 2019 Jul 10;38(15):2783-2796. doi: 10.1002/sim.8141. Epub 2019 Mar 25.
The inverse probability weighted estimator is often applied to two-phase designs and regression with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood-based estimators but, in general, are more robust against model misspecification. In this paper, we propose a best linear inverse probability weighted estimator for two-phase designs and missing covariate regression. Our proposed estimator is the projection of the SIPW onto the orthogonal complement of the score space based on a working regression model of the observed covariate data. The efficiency gain is from the use of the association between the outcome variable and the available covariates, which is the working regression model. One advantage of the proposed estimator is that there is no need to calculate the augmented term of the augmented weighted estimator. The estimator can be applied to general missing data problems or two-phase design studies in which the second phase data are obtained in a subcohort. The method can also be applied to secondary trait case-control genetic association studies. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via extensive simulation studies. The methods are applied to a bladder cancer case-control study.
逆概率加权估计器常用于两阶段设计和缺失协变量的回归。逆概率加权估计器通常比基于似然的估计器效率低，但通常对模型误设定更稳健。在本文中，我们提出了一种用于两阶段设计和缺失协变量回归的最佳线性逆概率加权估计器。我们提出的估计器是基于观察协变量数据的工作回归模型，将 SIPW 投影到得分空间的正交补集上得到的。效率的提高来自于对结果变量与可用协变量之间的关联的利用，这是工作回归模型。该估计器的一个优点是不需要计算增广加权估计器的增广项。该估计器可用于一般缺失数据问题或两阶段设计研究，其中第二阶段数据是在子队列中获得的。该方法还可用于二级性状病例对照遗传关联研究。推导了渐近分布，并通过广泛的模拟研究检验了所提出的估计器的有限样本性能。该方法应用于膀胱癌病例对照研究。
