Model-Assisted Regression Estimators for Longitudinal Data with Nonignorable Dropout
Model-Assisted Regression Estimators for Longitudinal Data with Nonignorable Dropout
复制标题
用于具有不可忽略丢失的纵向数据的模型辅助回归估计器
DOI：
10.1111/insr.12288
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发表时间：
2019
影响因子：
2
通讯作者：
Shao Jun
中科院分区：
文献类型：
--
作者：
Wang Lei;Qi Cuicui;Shao Jun
当Y和协变量X的联合分布是非参数的，且（Y，X）条件下的dropout倾向是参数的情况下，考虑具有不可忽略dropout的纵向估计。我们应用广义矩量方法来估计不可忽略的辍学倾向中的参数，基于使用工具z构建的估计方程，该工具z是x的一部分，与y相关，但与y无关。对y的非参数分布的总体均值和其他参数可以用估计的倾向进行逆倾向加权来估计。为了提高效率，我们推导了一个模型辅助回归估计器，利用协变量提供的信息和以前在纵向设置中观察到的dy值。当工作模型正确且满足其他条件时，模型辅助回归估计器是渐近正态的，并且更有效。通过仿真研究了估计器的有限样本性能，并给出了在HIV - CD4数据集上的应用作为说明。
Estimation with longitudinalYhaving nonignorable dropout is considered when the joint distribution ofYand covariateXis nonparametric and the dropout propensity conditional on (Y,X) is parametric. We apply the generalised method of moments to estimate the parameters in the nonignorable dropout propensity based on estimating equations constructed using an instrumentZ, which is part ofXrelated toYbut unrelated to the dropout propensity conditioned onYand other covariates. Population means and other parameters in the nonparametric distribution ofYcan be estimated based on inverse propensity weighting with estimated propensity. To improve efficiency, we derive a model‐assisted regression estimator making use of information provided by the covariates and previously observedY‐values in the longitudinal setting. The model‐assisted regression estimator is protected from model misspecification and is asymptotically normal and more efficient when the working models are correct and some other conditions are satisfied. The finite‐sample performance of the estimators is studied through simulation, and an application to the HIV‐CD4 data set is also presented as illustration.