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.
Biostatistics and Clinical Science Groups, F. Hoffman La Roche, Welwyn, UK. les.huson@roche.com
L W
LW
Huson
J
J
Chung
M
M
Salgo
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