Xiaoyue Niu1, Amy Zhang, Tim Brown, Robert Puckett, Mary Mahy, Le Bao. 1. aDepartment of Statistics, Pennsylvania State University, University Park, Pennsylvania bEast-West Center, Honolulu, Hawaii, USA cStrategic Information and Evaluation Department, UNAIDS, Geneva, Switzerland.
Abstract
OBJECTIVES: The article aims to give Spectrum/estimation and projection package (EPP) users and the scientific community a basic understanding of the underlying statistical model used to incorporate hierarchical structure in HIV subnational estimation, and to show how it has been implemented in the Spectrum/EPP interface for improving subepidemic estimation. The article also provides recommended default settings for this new model. METHODS: We apply a generalized linear mixed-effects model on antenatal clinics prevalence data to get area-specific prevalence and uncertainty estimates, and transform those estimates to auxiliary data. We then fit the EPP model to both the observed data and auxiliary data. RESULTS: We apply the proposed methods to four countries with different levels of data availability. We compare the out-of-sample prediction accuracy of the proposed method with varying auxiliary sample sizes and EPP without auxiliary data. CONCLUSION: We find that borrowing information from data-rich areas to data-sparse areas using our proposed method improves EPP fit in data-sparse areas. We recommend using the sample size estimated from generalized linear mixed-effects model as the default auxiliary sample size.
OBJECTIVES: The article aims to give Spectrum/estimation and projection package (EPP) users and the scientific community a basic understanding of the underlying statistical model used to incorporate hierarchical structure in HIV subnational estimation, and to show how it has been implemented in the Spectrum/EPP interface for improving subepidemic estimation. The article also provides recommended default settings for this new model. METHODS: We apply a generalized linear mixed-effects model on antenatal clinics prevalence data to get area-specific prevalence and uncertainty estimates, and transform those estimates to auxiliary data. We then fit the EPP model to both the observed data and auxiliary data. RESULTS: We apply the proposed methods to four countries with different levels of data availability. We compare the out-of-sample prediction accuracy of the proposed method with varying auxiliary sample sizes and EPP without auxiliary data. CONCLUSION: We find that borrowing information from data-rich areas to data-sparse areas using our proposed method improves EPP fit in data-sparse areas. We recommend using the sample size estimated from generalized linear mixed-effects model as the default auxiliary sample size.
Authors: Tim Brown; Le Bao; Jeffrey W Eaton; Daniel R Hogan; Mary Mahy; Kimberly Marsh; Bradley M Mathers; Robert Puckett Journal: AIDS Date: 2014-11 Impact factor: 4.177
Authors: Jeffrey W Eaton; Tim Brown; Robert Puckett; Robert Glaubius; Kennedy Mutai; Le Bao; Joshua A Salomon; John Stover; Mary Mahy; Timothy B Hallett Journal: AIDS Date: 2019-12-15 Impact factor: 4.177