Multiple polyexponentials and quasipolynomials as empirical nonlinear regression models: a case study with HIV viral load data.
Measurements of HIV1-RNA plasma concentrations are an important method of assessing patient response to anti-HIV1 treatment, and in most clinical trials of such treatments HIV1-RNA levels are assessed at regular intervals of time. HIV1-RNA levels in successfully treated patients tend to follow a standard pattern of biphasic decline-a rapid early decline in viral load, followed by a period of slower decline or a steady level. Fitting nonlinear regression models to these patterns of declining HIV1-RNA levels can be of value in comparing different treatment regimes and in predicting treatment outcome. Simple exponential-decline models can give an adequate fit to the typical pattern of HIV1-RNA decline, but we have explored the extent to which curve-fitting can be improved by using two novel nonlinear model forms. Specifically, we describe the fitting of multiple polyexponential and quasipolynomial forms to longitudinal HIV1-RNA plasma data collected in two recent trials of the novel anti-HIV1 treatment Fuzeon. We comment on the practicalities of fitting these nonlinear models, and compare the fit using various criteria.
Huson
L W
LW
Biostatistics and Clinical Science Groups, F. Hoffman La Roche, Welwyn, UK. les.huson@roche.com
Chung
J
J
Salgo
M
M
D003627:Data Interpretation, Statistical; D015497:HIV-1 / Q000302:isolation & purification*; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D012367:RNA, Viral / Q000032:analysis*; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012044:Regression Analysis; D019562:Viral Load / Q000706:statistics & numerical data*
D012367:RNA, Viral