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Statistical Methods in Medical Research, Vol. 11, No. 4, 317-325 (2002)
DOI: 10.1191/0962280202sm292ra

Mixture models for quantitative HIV RNA data

Lawrence H Moulton

Departments of International Health and Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA, LMOULTON{at}JHSPH.EDU

Frank C Curriero

Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Paulo F Barroso

Hospital Universitario Clementino Fraga Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

Clinical investigators are increasing their use of quantitative determinations of HIV viral load in their study populations. The distributions of these measures may be highly skewed, left-censored, and with an extra spike below the detection limit of the assay. We recommend use of a mixture model in this situation, with two sets of explanatory covariates. We extend this model to incorporate multiple measures across time, and to employ shared parameters as a way of increasing model efficiency and parsimony. Data from a cohort of HIV-infected men are used to illustrate these features, and simulations are performed to assess the utility of shared parameters.


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