  Longitudinal studies could be complicated by left-censored repeated measures.
For example, in Human Immunodeficiency Virus infection, there is a detection
limit of the assay used to quantify the plasma viral load. Simple imputation of
the limit of the detection or of half of this limit for left-censored measures
biases estimations and their standard errors. In this paper, we review two
likelihood-based methods proposed to handle left-censoring of the outcome in
linear mixed model. We show how to fit these models using SAS Proc NLMIXED and
we compare this tool with other programs. Indications and limitations of the
programs are discussed and an example in the field of HIV infection is shown.
