Stubbendick Amy L, Ibrahim Joseph G
Biogen, 14 Cambridge Center, Cambridge, Massachusetts 02142, USA.
Biometrics. 2003 Dec;59(4):1140-50. doi: 10.1111/j.0006-341x.2003.00131.x.
This article analyzes quality of life (QOL) data from an Eastern Cooperative Oncology Group (ECOG) melanoma trial that compared treatment with ganglioside vaccination to treatment with high-dose interferon. The analysis of this data set is challenging due to several difficulties, namely, nonignorable missing longitudinal responses and baseline covariates. Hence, we propose a selection model for estimating parameters in the normal random effects model with nonignorable missing responses and covariates. Parameters are estimated via maximum likelihood using the Gibbs sampler and a Monte Carlo expectation maximization (EM) algorithm. Standard errors are calculated using the bootstrap. The method allows for nonmonotone patterns of missing data in both the response variable and the covariates. We model the missing data mechanism and the missing covariate distribution via a sequence of one-dimensional conditional distributions, allowing the missing covariates to be either categorical or continuous, as well as time-varying. We apply the proposed approach to the ECOG quality-of-life data and conduct a small simulation study evaluating the performance of the maximum likelihood estimates. Our results indicate that a patient treated with the vaccine has a higher QOL score on average at a given time point than a patient treated with high-dose interferon.
本文分析了东部肿瘤协作组（ECOG）一项黑色素瘤试验中的生活质量（QOL）数据，该试验比较了神经节苷脂疫苗治疗与高剂量干扰素治疗的效果。由于存在几个难点，即不可忽略的纵向反应缺失和基线协变量，对该数据集的分析具有挑战性。因此，我们提出了一种选择模型，用于估计具有不可忽略的反应和协变量缺失的正态随机效应模型中的参数。参数通过使用吉布斯采样器和蒙特卡罗期望最大化（EM）算法的最大似然估计来确定。标准误差使用自助法计算。该方法允许反应变量和协变量中存在非单调的缺失数据模式。我们通过一系列一维条件分布对缺失数据机制和缺失协变量分布进行建模，允许缺失协变量为分类变量或连续变量，以及随时间变化的变量。我们将所提出的方法应用于ECOG生活质量数据，并进行了一项小型模拟研究，评估最大似然估计的性能。我们的结果表明，在给定时间点，接受疫苗治疗的患者平均生活质量得分高于接受高剂量干扰素治疗的患者。
