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Biostatistics Advance Access originally published online on March 28, 2006
Biostatistics 2007 8(1):53-71; doi:10.1093/biostatistics/kxj033
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

A mutagenetic tree hidden Markov model for longitudinal clonal HIV sequence data

Niko Beerenwinkel*

Department of Mathematics, University of California, 1073 Evans Hall, Berkeley, CA 94720 USA niko{at}math.berkeley.edu

Mathias Drton

Department of Statistics, The University of Chicago, 5734 S. University Avenue, Chicago, IL 60637, USA

* To whom correspondence should be addressed.

RNA viruses provide prominent examples of measurably evolving populations. In human immunodeficiency virus (HIV) infection, the development of drug resistance is of particular interest because precise predictions of the outcome of this evolutionary process are a prerequisite for the rational design of antiretroviral treatment protocols. We present a mutagenetic tree hidden Markov model for the analysis of longitudinal clonal sequence data. Using HIV mutation data from clinical trials, we estimate the order and rate of occurrence of seven amino acid changes that are associated with resistance to the reverse transcriptase inhibitor efavirenz.

Keywords: EM algorithm; Graphical model; Hidden Markov model; HIV drug resistance; Longitudinal data; Measurably evolving populations; Mutagenetic tree


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