Expected estimating equations to accommodate covariate measurement error
Expected estimating equations to accommodate covariate measurement error
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DOI：
10.1111/1467-9868.00247
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
C. Wang;Margaret Sullivan Pepe
中科院分区：
文献类型：
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作者：
C. Wang;Margaret Sullivan Pepe
估计方程不一定是基于似然的分数方程，在估计回归模型参数方面变得越来越流行。本文讨论了当所有研究对象的真实协变量数据均缺失，但存在替代或误测协变量时，基于一般估计方程的估计问题。该方法是由纵向数据的边际或部分条件回归中的协变量测量误差问题激发的。我们建议将估计基于以可用数据为条件的完整数据估计方程的期望。通过求解所得的估计方程，同时估计回归参数和其它干扰参数。如果完整数据得分是似然得分，并且条件是关于所有可用数据，则期望估计方程（EEE）估计量等于最大似然估计量。还研究了一种计算量较小的伪矩估计量。渐近分布理论推导。当误差过程为一阶自回归模型时，进行了小样本仿真。回归校准扩展到这个设置，并与非线性方法进行比较。我们展示的数据从纵向研究的儿童生长和成人肥胖之间的关系的方法。
Estimating equations which are not necessarily likelihood‐based score equations are becoming increasingly popular for estimating regression model parameters. This paper is concerned with estimation based on general estimating equations when true covariate data are missing for all the study subjects, but surrogate or mismeasured covariates are available instead. The method is motivated by the covariate measurement error problem in marginal or partly conditional regression of longitudinal data. We propose to base estimation on the expectation of the complete data estimating equation conditioned on available data. The regression parameters and other nuisance parameters are estimated simultaneously by solving the resulting estimating equations. The expected estimating equation (EEE) estimator is equal to the maximum likelihood estimator if the complete data scores are likelihood scores and conditioning is with respect to all the available data. A pseudo‐EEE estimator, which requires less computation, is also investigated. Asymptotic distribution theory is derived. Small sample simulations are conducted when the error process is an order 1 autoregressive model. Regression calibration is extended to this setting and compared with the EEE approach. We demonstrate the methods on data from a longitudinal study of the relationship between childhood growth and adult obesity.