Bivariate longitudinal model for the analysis of the evolution of HIV RNA and CD4 cell count in HIV infection taking into account left censoring of HIV RNA measures.

We present a bivariate linear mixed model taking into account censored measures of the response variable due to lower quantification limit of the assays. It allows an estimate of the correlation between the two response variables and takes into account this correlation for the estimation of other model parameters. This model was applied in a large cohort study (APROCO Cohort) to study the evolution under antiretroviral treatment of the two major biomarkers of the progression of Human Immunodeficiency Virus (HIV) infection: plasma HIV RNA and CD4+ T lymphocytes cell count.

Bone marrow histopathologic and molecular staging in epidermotropic T-cell lymphomas.

This study was undertaken to determine the prognostic value of bone marrow histopathologic and molecular analyses in 53 patients with mycosis fungoides and 7 with Sézary syndrome. Bone marrow was involved in only 1 patient with Sézary syndrome, clinical stage IVA, before bone marrow biopsy. An ambiguous T-cell infiltrate was observed in 8 patients but was not associated with disease progression.

Changes over calendar time in the risk of specific first AIDS-defining events following HIV seroconversion, adjusting for competing risks.

Background: Although studies have reported large reductions in the risks of AIDS and death since the introduction of potent anti-retroviral therapies, few have evaluated whether this has been similar for all AIDS-defining diseases. We wished to evaluate changes over time in the risk of specific AIDS-defining diseases, as first events, using data from individuals with known dates of HIV seroconversion.

Methods: Using a competing risks proportional hazards model on pooled data from 20 cohorts (CASCADE), we evaluated time from HIV seroconversion to each first AIDS-defining disease (16 groups) and to death without AIDS for four calendar periods, adjusting for exposure category, age, sex, acute infection, and stratifying by cohort.

Bivariate linear mixed models using SAS proc MIXED.

Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers. In this paper, we present a bivariate linear mixed model including random effects or first-order auto-regressive process and independent measurement error for both markers. Codes and tricks to fit these models using SAS Proc MIXED are provided.

Evaluation of cardiovascular risk factors in HIV-1 infected patients using carotid intima-media thickness measurement.

Background: Highly active antiretroviral therapies (HAART) in HIV-infected patients are often associated with lipodystrophy syndrome and metabolic disorders. Atherogenic lipid profile could expose these patients to atheromatous cardiovascular disease. We describe carotid artery intima-media thickness (IMT), a surrogate marker of atherosclerosis, according to HIV status, antiretroviral treatment, lipodystrophy and conventional cardiovascular risk factors.

Predictors of long-term increase in CD4(+) cell counts in human immunodeficiency virus-infected patients receiving a protease inhibitor-containing antiretroviral regimen.

The temporal relationships between plasma human immunodeficiency virus (HIV) RNA levels and evolution of CD4(+) cell counts was studied, using a 2-slope longitudinal mixed model, in 988 patients prospectively enrolled at the initiation of a protease inhibitor--containing regimen of antiretroviral therapy. The short-term slope (baseline through month 4) for mean change in CD4(+) cell count was +21.2 cells/mm(3)/month, and the long-term slope (month 4 through month 24) was +5.