关键词: 非线性混合效应模型, 一阶线性化算法(FO), 条件一阶线性化算法(FOCE), 改进的FOCE算法

Abstract: Nonlinear mixed effects model (NLMEM) is the model in which both the fixed and random effects occur nonlinearly in the model function. First-order linearization algorithm (FO) and conditional first-order linearization algorithm (FOCE) are two commonly used linearization algorithms to calculate the parameters in NLMEM. We proposed an improved method for calculating random effects parameters based on FOCE algorithm in this study. We also analyzed and compared the three algorithms using height growth data set and simulation data sets. The results are: random effects parameters obtained from improved FOCE algorithm can more really reflected the individual random variations and also make a high efficient fit.

Key words: nonlinear mixed effects model(NLMEM), first-order linearization algorithm(FO), conditional first-order linearization algorithm(FOCE), improved FOCE algorithm