Analysing repeated measurements with possibly missing observations by modelling marginal distributions.
Analysing repeated measurements with possibly missing observations by modelling marginal distributions.
复制标题
通过对边际分布建模来分析可能丢失观测值的重复测量值。
作者：
L. Wei;D. Stram
假设受试者在一组共同的时间点上重复观察，可能存在时间依赖性协变量和可能缺失的观察结果。在每个时间点，我们使用McCullagh和Nelder研究的一类准似然模型来模拟响应变量的边际分布和协变量对该分布的影响。没有参数模型的依赖性的重复观察的主题是假设。对于大样本，在一组预定的时间点的时间特异性回归系数的准似然估计被证明是近似联合正常。这与各种推理程序相结合，提供了关于整个研究期间协变量对响应变量的影响的全局图。还提供了用于测试假设的准似然模型的充分性的拟合不足测试。这里考虑的所有方法都用现实生活中的例子来说明。
Suppose that subjects are observed repeatedly over a common set of time points with possibly time-dependent covariates and possibly missing observations. At each time point we model the marginal distribution of the response variable and the effect of the covariates on that distribution using a class of quasi-likelihood models studied in McCullagh and Nelder. No parametric model of dependence of the repeated observations of the subject is assumed. For large samples, the quasi-likelihood estimates of the time-specific regression coefficients over the set of predetermined time points are shown to be approximately jointly normal. This, coupled with various inference procedures, provides a global picture about the effects of the covariates on the response variable over the entire study period. A lack-of-fit test for testing the adequacy of the assumed quasi-likelihood model is also provided. All the methods considered here are illustrated with real-life examples.