UNEQUALLY SPACED LONGITUDINAL DATA WITH AR(1) SERIAL-CORRELATION
UNEQUALLY SPACED LONGITUDINAL DATA WITH AR(1) SERIAL-CORRELATION
复制标题
DOI：
10.2307/2532504
复制
发表时间：
1991-03-01
期刊：
影响因子：
1.9
通讯作者：
BOADIBOATENG, F
中科院分区：
文献类型：
--
作者：
JONES, RH;BOADIBOATENG, F
本文讨论了在不同的不等间隔时间点观察每个受试者时的纵向数据分析。被试的观察结果要么不相关，要么具有连续时间一阶自回归结构，可能存在观察误差。假设随机系数具有任意的主体间协方差矩阵。协变量可以包含在模型的固定效应部分。使用卡尔曼滤波器计算未知参数的精确最大似然估计来评估似然，然后使用非线性优化程序使其最大化。给出了一个在少量观察时间内观察大量受试者的例子。选择最佳模型的假设检验采用Wald’s对比检验或基于拟合完整模型和受限模型的似然比检验。
This paper discusses longitudinal data analysis when each subject is observed at different unequally spaced time points. Observations within subjects are assumed to be either uncorrelated or to have a continuous-time first-order autoregressive structure, possibly with observation error. The random coefficients are assumed to have an arbitrary between-subject covariance matrix. Covariates can be included in the fixed effects part of the model. Exact maximum likelihood estimates of the unknown parameters are computed using the Kalman filter to evaluate the likelihood, which is then maximized with a nonlinear optimization program. An example is presented where a large number of subjects are each observed at a small number of observation times. Hypothesis tests for selecting the best model are carried out using Wald's test on contrasts or likelihood ratio tests based on fitting full and restricted models.