De Gruttola V, Tu X M
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.
Biometrics. 1994 Dec;50(4):1003-14.
The purpose of this article is to model the progression of CD4-lymphocyte count and the relationship between different features of this progression and survival time. The complicating factors in this analysis are that the CD4-lymphocyte count is observed only at certain fixed times and with a high degree of measurement error, and that the length of the vector of observations is determined, in part, by the length of survival. If probability of death depends on the true, unobserved CD4-lymphocyte count, then the survival process must be modelled. Wu and Carroll (1988, Biometrics 44, 175-188) proposed a random effects model for two-sample longitudinal data in the presence of informative censoring, in which the individual effects included only slopes and intercepts. We propose methods for fitting a broad class of models of this type, in which both the repeated CD4-lymphocyte counts and the survival time are modelled using random effects. These methods permit us to estimate parameters describing the progression of CD4-lymphocyte count as well as the effect of differences in the CD4 trajectory on survival. We apply these methods to results of AIDS clinical trials.
本文的目的是对CD4淋巴细胞计数的变化过程以及该变化过程的不同特征与生存时间之间的关系进行建模。该分析中的复杂因素在于，CD4淋巴细胞计数仅在某些固定时间进行观测，且存在高度的测量误差，并且观测向量的长度部分取决于生存时间的长短。如果死亡概率取决于真实的、未观测到的CD4淋巴细胞计数，那么就必须对生存过程进行建模。Wu和Carroll（1988年，《生物统计学》44卷，175 - 188页）针对存在信息删失情况下的两样本纵向数据提出了一种随机效应模型，其中个体效应仅包括斜率和截距。我们提出了用于拟合这类广泛模型的方法，其中使用随机效应同时对重复的CD4淋巴细胞计数和生存时间进行建模。这些方法使我们能够估计描述CD4淋巴细胞计数变化过程的参数，以及CD4轨迹差异对生存的影响。我们将这些方法应用于艾滋病临床试验的结果。
