Random regression models for human immunodeficiency virus ribonucleic acid data subject to left censoring and informative drop-outs.

*(English)*Zbl 0965.62097Summary: Objectives in many longitudinal studies of individuals infected with the human immuno-deficiency virus (HIV) include the estimation of population average trajectories of HIV ribonucleic acid (RNA) over time and tests for differences in trajectory across subgroups. Special features that are often inherent in the underlying data include a tendency for some HIV RNA levels to be below an assay detection limit, and for individuals with high initial levels or high rates of change to drop out of the study early because of illness or death.

We develop a likelihood for the observed data that incorporates both of these features. Informative drop-outs are handled by means of an approach previously published by M.D. Schluchter [Stat. Med. 11, 1861-1870 (1992)]. Using data from the HIV Epidemiology Research Study, we implement a maximum likelihood procedure to estimate initial HIV RNA levels and slopes within a population, compare these parameters across subgroups of HIV-infected women and illustrate the importance of appropriate treatment of left censoring and informative drop-outs. We also assess model assumptions and consider the prediction of random intercepts and slopes in this setting. The results suggest that marked bias in estimates of fixed effects, variance components and standard errors in the analysis of HIV RNA data might be avoided by the use of methods like those illustrated.

We develop a likelihood for the observed data that incorporates both of these features. Informative drop-outs are handled by means of an approach previously published by M.D. Schluchter [Stat. Med. 11, 1861-1870 (1992)]. Using data from the HIV Epidemiology Research Study, we implement a maximum likelihood procedure to estimate initial HIV RNA levels and slopes within a population, compare these parameters across subgroups of HIV-infected women and illustrate the importance of appropriate treatment of left censoring and informative drop-outs. We also assess model assumptions and consider the prediction of random intercepts and slopes in this setting. The results suggest that marked bias in estimates of fixed effects, variance components and standard errors in the analysis of HIV RNA data might be avoided by the use of methods like those illustrated.

##### MSC:

62P10 | Applications of statistics to biology and medical sciences; meta analysis |

92C50 | Medical applications (general) |

62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |