Wei L J, Stram D O
Department of Biostatistics, University of Michigan, Ann Arbor 48109.
Stat Med. 1988 Jan-Feb;7(1-2):139-48. doi: 10.1002/sim.4780070115.
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.
假设在一组共同的时间点上对受试者进行重复观察，这些时间点可能存在随时间变化的协变量，并且可能存在缺失观测值。在每个时间点，我们使用McCullagh和Nelder研究的一类拟似然模型对响应变量的边际分布以及协变量对该分布的影响进行建模。未假设受试者重复观测值的参数依赖模型。对于大样本，在预定时间点集上特定时间回归系数的拟似然估计显示近似联合正态分布。这与各种推断程序相结合，提供了关于协变量在整个研究期间对响应变量影响的全局图景。还提供了用于检验假设的拟似然模型是否充分的失拟检验。这里考虑的所有方法都用实际例子进行了说明。
