Literature DB >> 12820282

Age-specific back-projection of HIV diagnosis data.

Niels G Becker1, James J C Lewis, Zhengfeng Li, Ann McDonald.   

Abstract

A method for reconstructing the HIV infection curve from data on both HIV and AIDS diagnoses is enhanced by using age as a covariate and by using the diagnosis data to estimate parameters that were previously assumed known. Maximum likelihood estimation is used for parameters of the induction distribution. Each of the set of parameters that specify the baseline rate of infection over time and the set of parameters giving the relative susceptibility over age are estimated by maximizing the likelihood subject to a smoothness requirement. We find that estimating the extra parameters is feasible, producing estimates with good precision. Including age as a covariate gives 90 per cent confidence intervals for the HIV incidence curve that are about 20 per cent narrower than those obtained when age data are not used. Copyright 2003 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Year:  2003        PMID: 12820282     DOI: 10.1002/sim.1406

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  7 in total

Review 1.  How can we better identify early HIV infections?

Authors:  Nora E Rosenberg; Christopher D Pilcher; Michael P Busch; Myron S Cohen
Journal:  Curr Opin HIV AIDS       Date:  2015-01       Impact factor: 4.283

2.  A Bayesian hierarchical model with novel prior specifications for estimating HIV testing rates.

Authors:  Qian An; Jian Kang; Ruiguang Song; H Irene Hall
Journal:  Stat Med       Date:  2015-11-15       Impact factor: 2.373

3.  Bayesian evidence synthesis for a transmission dynamic model for HIV among men who have sex with men.

Authors:  A M Presanis; D De Angelis; A Goubar; O N Gill; A E Ades
Journal:  Biostatistics       Date:  2011-04-27       Impact factor: 5.899

4.  Extending Bayesian back-calculation to estimate age and time specific HIV incidence.

Authors:  Francesco Brizzi; Paul J Birrell; Martyn T Plummer; Peter Kirwan; Alison E Brown; Valerie C Delpech; O Noel Gill; Daniela De Angelis
Journal:  Lifetime Data Anal       Date:  2019-02-27       Impact factor: 1.588

5.  Is back-projection methodology still relevant for estimating HIV incidence from national surveillance data?

Authors:  Kylie-Ann Mallitt; David P Wilson; Ann McDonald; Handan Wand
Journal:  Open AIDS J       Date:  2012-09-07

6.  Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data.

Authors:  Ard van Sighem; Fumiyo Nakagawa; Daniela De Angelis; Chantal Quinten; Daniela Bezemer; Eline Op de Coul; Matthias Egger; Frank de Wolf; Christophe Fraser; Andrew Phillips
Journal:  Epidemiology       Date:  2015-09       Impact factor: 4.822

Review 7.  Modeling methods for estimating HIV incidence: a mathematical review.

Authors:  Xiaodan Sun; Hiroshi Nishiura; Yanni Xiao
Journal:  Theor Biol Med Model       Date:  2020-01-22       Impact factor: 2.432

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.