A Comparison of Seven Cox Regression-Based Models to Account for Heterogeneity Across Multiple HIV Treatment Cohorts in Latin America and the Caribbean

Simple item page
dc.contributor.authorGiganti, Mark J.
dc.contributor.authorLuz, Paula M.
dc.contributor.authorCaro-Vega, Yanink
dc.contributor.authorCesar, Carina
dc.contributor.authorPadgett, Denis
dc.contributor.authorKoenig, Serena
dc.contributor.authorEchevarria, Juan
dc.contributor.authorMcGowan, Catherine C
dc.contributor.authorShepherd, Bryan E
dc.date.accessioned2024-05-22T10:19:29Z
dc.date.available2024-05-22T10:19:29Z
dc.date.issued2015-5
dc.description.abstractMany studies of HIV/AIDS aggregate data from multiple cohorts to improve power and generalizability. There are several analysis approaches to account for cross-cohort heterogeneity; we assessed how different approaches can impact results from an HIV/AIDS study investigating predictors of mortality. Using data from 13,658 HIV-infected patients starting antiretroviral therapy from seven Latin American and Caribbean cohorts, we illustrate the assumptions of seven readily implementable approaches to account for across cohort heterogeneity with Cox proportional hazards models, and we compare hazard ratio estimates across approaches. As a sensitivity analysis, we modify cohort membership to generate specific heterogeneity conditions. Hazard ratio estimates varied slightly between the seven analysis approaches, but differences were not clinically meaningful. Adjusted hazard ratio estimates for the association between AIDS at treatment initiation and death varied from 2.00 to 2.20 across approaches that accounted for heterogeneity; the adjusted hazard ratio was estimated as 1.73 in analyses that ignored across cohort heterogeneity. In sensitivity analyses with more extreme heterogeneity, we noted a slightly greater distinction between approaches. Despite substantial heterogeneity between cohorts, the impact of the specific approach to account for heterogeneity was minimal in our case study. Our results suggest that it is important to account for across cohort heterogeneity in analyses, but that the specific technique for addressing heterogeneity may be less important. Because of their flexibility in accounting for cohort heterogeneity, we prefer stratification or meta-analysis methods, but we encourage investigators to consider their specific study conditions and objectives.
dc.identifier.citationAIDS Res Hum Retroviruses. 2015 May;31(5):496-503. doi: 10.1089/AID.2014.0241
dc.identifier.other10.1089/aid.2014.0241
dc.identifier.urihttps://repositorio.huesped.org.ar/handle/123456789/1027
dc.relation.ispartofseriesAIDS Research and Human Retroviruses; 31(5)
dc.subjectComparison
dc.subjectCox Regression-Based Models
dc.subjectHeterogeneity
dc.titleA Comparison of Seven Cox Regression-Based Models to Account for Heterogeneity Across Multiple HIV Treatment Cohorts in Latin America and the Caribbean

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
78_2015_Giganti_ A Comparison of Seven Cox Regression-Based Models to Account for Heterogeneity Across Multiple HIV Treatment Cohorts.pdf
Size:
377.8 KB
Format:
Adobe Portable Document Format
Download

Collections

Articulos científicos