Bayesian Inference Of Mixed Effect Model Based On HIV Antiviral Data

HIV is a human immunodeficiency virus.It adheres to CD4⁺T cells and replicate continuously,leading to the collapse of the human immune system.HAART is the main means of treating AIDS.After treatment,the viral load of most infectors will decrease rapidly.Meanwhile,CD4⁺T cells will increase and the immune system will recover.However,due to drug resistance or other reasons,the viral load of some patients rebounded later in treatment.Moreover,the initial number of CD4⁺T cells reflects immune state before treatment,and the value may also affect the treatment effect.Therefore,this paper mainly considers the relationship between viral load and CD4⁺T cell number under different types of data through joint modeling,and provides a theoretical reference for clinical treatment,which is as follows:Aiming at the rebound and left-slitting of viral load and discrete CD4⁺T cell number in HIV studies,combining semi-parametric nonlinear mixed-effects model with variable points and generalized linear mixed-effect model,studying the relationship between them through mixed modeling.Based on the MCMC algorithm,estimate parameters by joint Bayesian approach(JBA and compared with the two-step Bayesian approach(TSBA and the naive Bayes approach(NBA.In addition,R software is used for numerical simulation and analysis,the results show that the JBA proposed in this paper has smaller standard deviation and better statistical properties.Considering the effect of initial CD4⁺T cells(CD4_i0 on the number of CD4⁺T cells after antiviral treatment.Specifically,a multivariate linear mixed-effects model,which containingCD4_{i 0},t_ij and t_ij²,was used to fit CD4⁺T cells after anti-HIV treatment.And it was incorporated into the response model as a covariate model for analysis.Win BUGS was used to achieve Bayesian estimation of the parameters.By comparing with the existing joint models,to evaluated the goodness of fit of the proposed model.Case analysis and numerical simulation show that the goodness of fit of the model withCD4_i0 is better,which indicates CD4_i0 is an important factor to evaluate the number of CD4⁺T cells after treatment.