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NLMEM: a new SAS/IML macro for hierarchical nonlinear models.

Abstract：

:Analysis of longitudinal data is one of the most challenging tasks in statistical modeling. In the analysis, it is often necessary to take into account nonlinear response to a set of parameters of interest and correlation between measurements taken from the same individual. In addition, between- and within-subject variation has to be handled properly. An example of addressing these issues is the hierarchical nonlinear model, where parameter estimation can be performed using linearization method. In this paper a new NLMEM SAS/IML macro for hierarchical nonlinear models is proposed. The program uses a portion of the code developed earlier in NLINMIX. NLMEM retains all the benefits of NLINMIX while allowing the systematic part of the model structure to be specified using IML syntax. Consequently, NLMEM allows estimation of models which are not tractable using NLINMIX. In particular, it allows us to address advanced population pharmacokinetics and pharmacodynamics models specified by ordinary differential equations.

journal_name

Comput Methods Programs Biomed

journal_title

Computer methods and programs in biomedicine

authors

Galecki AT

doi

10.1016/s0169-2607(97)00066-7

subject

Has Abstract

pub_date

1998-03-01 00:00:00

pages

207-16

issue

3

eissn

0169-2607

issn

1872-7565

pii

S0169260797000667

journal_volume

55

pub_type

杂志文章

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