Data on serologic inflammatory biomarkers assessed using multiplex assays and host characteristics in the Multicenter AIDS Cohort Study (MACS)

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Abstract

This article contains data on the associations between fixed and modifiable host characteristics and twenty-three biomarkers of inflammation and immune activation measured longitudinally in a cohort of 250 HIV-uninfected men from the Multicenter AIDS Cohort Study (1984–2009) after adjusting for age, study site, and blood draw time of day using generalized gamma regression. This article also presents associations between each biomarker and each host characteristic in a sample restricted to 2001–2009. These data are supplemental to our original research article entitled “Host factors associated with serologic inflammatory markers assessed using multiplex assays” (McKay, S. Heather, Bream, H. Jay, Margolick, B. Joseph, Martínez-Maza, Otoniel, Phair, P. John, Rinaldo, R. Charles, Abraham, G. Alison, L.P. Jacobson, 2016) [1].

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Parametric survival analysis and taxonomy of hazard functions for the generalized gamma distribution.

Haitao Chu, F. Schneider, Alvaro Muñoz-Noval … (2007)

The widely used Cox proportional hazards regression model for the analysis of censored survival data has limited utility when either hazard functions themselves are of primary interest, or when relative times instead of relative hazards are the relevant measures of association. Parametric regression models are an attractive option in situations such as this, although the choice of a particular model from the available families of distributions can be problematic. The generalized gamma (GG) distribution is an extensive family that contains nearly all of the most commonly used distributions, including the exponential, Weibull, log normal and gamma. More importantly, the GG family includes all four of the most common types of hazard function: monotonically increasing and decreasing, as well as bathtub and arc-shaped hazards. We present here a taxonomy of the hazard functions of the GG family, which includes various special distributions and allows depiction of effects of exposures on hazard functions. We applied the proposed taxonomy to study survival after a diagnosis of clinical AIDS during different eras of HIV therapy, where proportionality of hazard functions was clearly not fulfilled and flexibility in estimating hazards with very different shapes was needed. Comparisons of survival after AIDS in different eras of therapy are presented in terms of both relative times and relative hazards. Standard errors for these and other derived quantities are computed using the delta method and checked using the bootstrap. Description of standard statistical software (Stata, SAS and S-Plus) for the computations is included and available at http://statepi.jhsph.edu/software.

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The Multicenter AIDS Cohort Study: retention after 9 1/2 years.

J Chmiel, Callie R Thackeray, H S Metz … (1995)

In a longitudinal, multicenter study of 4,954 men at risk for human immunodeficiency virus infection and acquired immunodeficiency syndrome, data from the first 9.5 years of follow-up (April 1984 through September 1993) were used to determine differences between those who remained in the study and those who dropped out. Demographic variables (age, race, education, employment, and study center), health status (human immunodeficiency virus type 1 serostatus and depression), and behavioral characteristics (alcohol drinking, drug use, and anal-receptive intercourse) were analyzed. Strategies for promoting retention included having frequent contact with participants, generating trust, keeping participants well-informed, utilizing multiple resources for follow-up, and providing flexible methods of participation. After 9.5 years of follow-up, vital status was known for 4,385 (88.5%) of the participants. Results from multiple logistic regression showed that race, age, education, and smoking were each significantly associated with nonparticipation (p < 0.001). A high level of retention was maintained in this well-educated and highly motivated cohort of homosexual/bisexual men. Extensive follow-up methods may improve case-finding. Nonwhite race, younger age, less education, and smoking were important predictors of dropping out. These findings identify specific groups for targeting follow-up efforts to reduce potential bias due to dropout.

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Author and article information

Contributors

Heather S. McKay

Journal

Journal ID (nlm-ta): Data Brief

Journal ID (iso-abbrev): Data Brief

Title: Data in Brief

Publisher: Elsevier

ISSN (Electronic): 2352-3409

Publication date PMC-release: 16 August 2016

Publication date Collection: December 2016

Publication date (Electronic): 16 August 2016

Volume: 9

Pages: 262-270

Affiliations

[a ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

[b ]W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

[c ]Departments of Obstetrics & Gynecology and Microbiology, Immunology & Molecular Genetics, David Geffen School of Medicine at UCLA, and Department of Epidemiology, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA

[d ]Northwestern University Feinberg School of Medicine, Chicago, IL, USA

[e ]Department of Molecular Virology and Microbiology, University of Pittsburgh School of Medicine, Pittsburgh, CA, USA

Author notes

[* ]Corresponding author. hmckay4@ 123456jhu.edu

Article

Publisher ID: S2352-3409(16)30520-0

DOI: 10.1016/j.dib.2016.08.019

PMC ID: 5024314

SO-VID: 168eb369-2802-45a9-9e4c-5386f91a42ca

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

History

Date received : 18 May 2016

Date revision received : 3 August 2016

Date accepted : 9 August 2016

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