Hogan Joseph W, Lin Xihong, Herman Benjamin
Center for Statistical Sciences, Department of Community Health, Brown University, Box G-H, Providence, Rhode Island 02912, USA.
Biometrics. 2004 Dec;60(4):854-64. doi: 10.1111/j.0006-341X.2004.00240.x.
The analysis of longitudinal repeated measures data is frequently complicated by missing data due to informative dropout. We describe a mixture model for joint distribution for longitudinal repeated measures, where the dropout distribution may be continuous and the dependence between response and dropout is semiparametric. Specifically, we assume that responses follow a varying coefficient random effects model conditional on dropout time, where the regression coefficients depend on dropout time through unspecified nonparametric functions that are estimated using step functions when dropout time is discrete (e.g., for panel data) and using smoothing splines when dropout time is continuous. Inference under the proposed semiparametric model is hence more robust than the parametric conditional linear model. The unconditional distribution of the repeated measures is a mixture over the dropout distribution. We show that estimation in the semiparametric varying coefficient mixture model can proceed by fitting a parametric mixed effects model and can be carried out on standard software platforms such as SAS. The model is used to analyze data from a recent AIDS clinical trial and its performance is evaluated using simulations.
由于信息性失访导致的缺失数据常常使纵向重复测量数据的分析变得复杂。我们描述了一种用于纵向重复测量联合分布的混合模型，其中失访分布可能是连续的，且响应与失访之间的依赖关系是半参数的。具体而言，我们假设响应在给定失访时间的条件下遵循变系数随机效应模型，其中回归系数通过未指定的非参数函数依赖于失访时间，当失访时间为离散时（例如对于面板数据）使用阶梯函数进行估计，当失访时间为连续时使用平滑样条进行估计。因此，在所提出的半参数模型下的推断比参数条件线性模型更稳健。重复测量的无条件分布是失访分布上的混合。我们表明，半参数变系数混合模型中的估计可以通过拟合参数混合效应模型来进行，并且可以在诸如SAS等标准软件平台上实现。该模型用于分析来自最近一项艾滋病临床试验的数据，并通过模拟评估其性能。
