作者: France Mentré , Roberto Gomeni
DOI: 10.1080/10543409508835104
关键词:
摘要: This article proposes an EM-like algorithm for estimating, by maximum likelihood, the population parameters of a nonlinear mixed-effect model given sparse individual data. The first step involves Bayesian estimation parameters. During second step, are estimated using linearization about those estimates. (implemented in P-PHARM) is evaluated on simulated data, mimicking pharmacokinetic analyses and compared to First-Order method Conditional Estimates (both implemented NONMEM). accuracy results, within few iterations, shows capabilities proposed approach