A regression modeling approach for describing patterns of HIV genetic variation.
We introduce a novel approach for describing patterns of HIV genetic variation using regression modeling techniques. Parameters are defined for describing genetic variation within and between viral populations by generalizing Simpson's index of diversity. Regression models are specified for these variation parameters and the generalized estimating equation framework is used for estimating both the regression parameters and their corresponding variances. Conditions are described under which the usual asymptotic approximations to the distribution of the estimators are met. This approach provides a formal statistical framework for testing hypotheses regarding the changing patterns of HIV genetic variation over time within an infected patient. The application of these methods for testing biologically relevant hypotheses concerning HIV genetic variation is demonstrated in an example using sequence data from a subset of patients from the Multicenter AIDS Cohort Study.
Mayer-Hamblett
N
N
Department of Biostatistics, University of Washington, Seattle 98195, USA. mayerh@u.washington.edu
Self
S
S
D000163:Acquired Immunodeficiency Syndrome / Q000821:virology; D015331:Cohort Studies; D014644:Genetic Variation; D006678:HIV / Q000235:genetics*; D015658:HIV Infections / Q000235:genetics / Q000503:physiopathology / Q000821:virology; D006801:Humans; D008957:Models, Genetic; D015337:Multicenter Studies as Topic; D012044:Regression Analysis