Lipsitz S R, Molenberghs G, Fitzmaurice G M, Ibrahim J
Dana Farber Cancer Institute, Boston, Massachusetts 02115, USA.
Biometrics. 2000 Jun;56(2):528-36. doi: 10.1111/j.0006-341x.2000.00528.x.
This paper considers a modification of generalized estimating equations (GEE) for handling missing binary response data. The proposed method uses Gaussian estimation of the correlation parameters, i.e., the estimating function that yields an estimate of the correlation parameters is obtained from the multivariate normal likelihood. The proposed method yields consistent estimates of the regression parameters when data are missing completely at random (MCAR). However, when data are missing at random (MAR), consistency may not hold. In a simulation study with repeated binary outcomes that are missing at random, the magnitude of the potential bias that can arise is examined. The results of the simulation study indicate that, when the working correlation matrix is correctly specified, the bias is almost negligible for the modified GEE. In the simulation study, the proposed modification of GEE is also compared to the standard GEE, multiple imputation, and weighted estimating equations approaches. Finally, the proposed method is illustrated using data from a longitudinal clinical trial comparing two therapeutic treatments, zidovudine (AZT) and didanosine (ddI), in patients with HIV.
本文考虑了用于处理缺失二元响应数据的广义估计方程（GEE）的一种改进方法。所提出的方法使用相关参数的高斯估计，即从多元正态似然性中获得产生相关参数估计值的估计函数。当数据完全随机缺失（MCAR）时，所提出的方法能得出回归参数的一致估计值。然而，当数据随机缺失（MAR）时，一致性可能不成立。在一项针对随机缺失的重复二元结果的模拟研究中，研究了可能出现的潜在偏差的大小。模拟研究结果表明，当工作相关矩阵被正确指定时，对于改进的GEE，偏差几乎可以忽略不计。在模拟研究中，还将所提出的GEE改进方法与标准GEE、多重填补和加权估计方程方法进行了比较。最后，使用来自一项纵向临床试验的数据说明了所提出方法，该试验比较了齐多夫定（AZT）和去羟肌苷（ddI）两种治疗方法对HIV患者的疗效。
