Bayesian multi-response nonlinear mixed-effect model: application of two recent HIV infection biomarkers

Castel Charlotte (), Sommen Cécile, Chatignoux Edouard, Le Strat Yann and Alioum Ahmadou
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Castel Charlotte: Direction Appui, Traitements et Analyses des données, Santé Publique France, 12 Rue du Val d’Osne, Saint-Maurice 94417, Île-de-France, France
Sommen Cécile: Direction Appui, Traitements et Analyses des données, Santé Publique France, 12 Rue du Val d’Osne, Saint-Maurice 94417, Île-de-France, France
Chatignoux Edouard: Direction Appui, Traitements et Analyses des données, Santé Publique France, 12 Rue du Val d’Osne, Saint-Maurice 94417, Île-de-France, France
Le Strat Yann: Direction Appui, Traitements et Analyses des données, Santé Publique France, 12 Rue du Val d’Osne, Saint-Maurice 94417, Île-de-France, France
Alioum Ahmadou: Bordeaux Population Health, Biostatistical Team, Inserm Center U1219, Bordeaux, France

The International Journal of Biostatistics, 2022, vol. 18, issue 2, 455-471

Abstract: Since the discovery of the human immunodeficiency virus (HIV) 35 years ago, the epidemic is still ongoing in France. To monitor the dynamics of HIV transmission and assess the impact of prevention campaigns, the main indicator is the incidence. One method to estimate the HIV incidence is based on biomarker values at diagnosis and their dynamics over time. Estimating the HIV incidence from biomarkers first requires modeling their dynamics since infection using external longitudinal data. The objective of the work presented here is to estimate the joint dynamics of two biomarkers from the PRIMO cohort. We thus jointly modeled the dynamics of two biomarkers (TM and V3) using a multi-response nonlinear mixed-effect model. The parameters were estimated using Bayesian Hamiltonian Monte Carlo inference. This procedure was first applied to the real data of the PRIMO cohort. In a simulation study, we then evaluated the performance of the Bayesian procedure for estimating the parameters of multi-response nonlinear mixed-effect models.

Keywords: Hamiltonian Monte Carlo inference; HIV biomarkers; multi-response model; nonlinear mixed models (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2021-0030

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