intra-cluster correlation estimates for hiv-related outcomes from care and treatment clinics in dar es salaam, tanzania

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ID: 213509
2016
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Abstract
Introduction: Researchers planning cluster-randomized controlled trials (cRCTs) require estimates of the intra-cluster correlation coefficient (ICC) from previous studies for sample size calculations. This paper fills a persistent gap in the literature by providing estimates of ICCs for many key HIV-related clinical outcomes. Methods: Data from HIV-positive patients from 47 HIV care and treatment clinics in Dar es Salaam, Tanzania were used to calculate ICCs by site of enrollment or site of ART initiation for various clinical outcomes using cross-sectional and longitudinal data. ICCs were estimated using linear mixed models where either clinic of enrollment or clinic of ART initiation served as the random effect. Results: ICCs ranged from 0 to 0.0706 (95% CI: 0.0447, 0.1098). For most outcomes, the ICCs were large enough to meaningfully affect sample size calculations. For binary outcomes, the ICCs for event prevalence at baseline tended to be larger than the ICCs for later cumulative incidences. For continuous outcomes, the ICCs for baseline values tended to be larger than the ICCs for the change in values from baseline. Conclusion: The ICCs for HIV-related outcomes cannot be ignored when calculating sample sizes for future cluster-randomized trials. The differences between ICCs calculated from baseline data alone and ICCs calculated using longitudinal data demonstrate the importance of selecting an ICC that reflects a study's intended design and duration for sample size calculations. While not generalizable to all contexts, these estimates provide guidance for future researchers seeking to design adequately powered cRCTs in Sub-Saharan African HIV treatment and care clinics.
Reference Key
barnhart2016contemporaryintra-cluster Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Dale Barnhart;Ellen Hertzmark;Enju Liu;Ester Mungure;Aisa N. Muya;David Sando;Guerino Chalamilla;Nzovu Ulenga;Till Bärnighausen;Wafaie Fawzi;Donna Spiegelman
Journal ingenierías usbmed
Year 2016
DOI
10.1016/j.conctc.2016.09.001
URL
http://www.sciencedirect.com/science/article/pii/S2451865416300436
Keywords
hiv infections multicenter studies

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