Multiple Polyexponentials and Quasipolynomials as Empirical Nonlinear Regression Models: A Case Study with HIV Viral Load Data |
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Authors: | L. W. Huson J. Chung M. Salgo |
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Affiliation: | 1. Biostatistics and Clinical Science Groups , F. Hoffman La Roche , Welwyn, UK les.huson@roche.com;3. Biostatistics and Clinical Science Groups , F. Hoffman La Roche , Nutley, NJ |
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Abstract: | 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. |
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Keywords: | HIV1 Nonlinear regression Polyexponential Quasipolynomial |
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