Nonlinear Mixed-Effects Models for HIV Viral Load Trajectories Before and After Antiretroviral Therapy Interruption, Incorporating Left Censoring

Characterizing features of the viral rebound trajectories and identifying host, virological, and immunological factors that are predictive of the viral rebound trajectories are central to HIV cure research. In this paper, we investigate if key features of HIV viral decay and CD4 trajectories during antiretroviral therapy (ART) are associated with characteristics of HIV viral rebound following ART interruption. Nonlinear mixed effect (NLME) models are used to model viral load trajectories before and following ART interruption, incorporating left censoring due to lower detection limits of viral load assays. A stochastic approximation EM (SAEM) algorithm is used for parameter estimation and inference. To circumvent the computational intensity associated with maximizing the joint likelihood, we propose an easy-to-implement threestep method. We evaluate the performance of this method through simulation studies and apply it to data from the Zurich Primary HIV Infection Study. We find that some key features of viral load during ART (e.g., viral decay rate) are significantly associated with important characteristics of viral rebound following ART interruption (e.g., viral set point)

Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring

Longitudinal studies are common in biomedical research, such as an HIV study. In an HIV study, the viral decay during an anti-HIV treatment and the viral rebound after the treatment is interrupted can be viewed as two longitudinal processes, and they may be related to each other. In this thesis, we investigate if key features of HIV viral decay and CD4 trajectories during antiretroviral therapy (ART) are associated with characteristics of HIV viral rebound following ART interruption. Motivated by a real AIDS dataset, two non-linear mixed effects (NLME) models are used to model the viral load trajectories before and following ART interruption, respectively, incorporating left censoring due to lower detection limits of viral load assays. A linear mixed effects (LME) model is used to model CD4 trajectories. The models may be linked through shared random effects, since these random effects reflect individual characteristics of the longitudinal processes. A stochastic approximation EM (SAEM) method is used for parameter estimation and inference. To reduce the computation burden associated with maximizing the joint likelihood, an easy-to-implement three-step (TS) method is proposed by using SAEM algorithm and bootstrap. Data analysis results show that some key features of viral load and CD4 trajectories during ART (e.g., viral decay rate) are significantly associated with important characteristics of viral rebound following ART interruption (e.g., viral set point). Simulation studies are conducted to evaluate the performances of the proposed TS method and the naive method, which still uses SAEM algorithm but substitutes the censored viral load values with half the detection limit and without bootstrap. It is concluded that the proposed TS method outperforms the naive method.Science, Faculty ofStatistics, Department ofGraduat

Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring

OBJECTIVES: Characterizing features of the viral rebound trajectories and identifying host, virological, and immunological factors that are predictive of the viral rebound trajectories are central to HIV cure research. We investigate if key features of HIV viral decay and CD4 trajectories during antiretroviral therapy (ART) are associated with characteristics of HIV viral rebound following ART interruption. METHODS: Nonlinear mixed effect (NLME) models are used to model viral load trajectories before and following ART interruption, incorporating left censoring due to lower detection limits of viral load assays. A stochastic approximation EM (SAEM) algorithm is used for parameter estimation and inference. To circumvent the computational intensity associated with maximizing the joint likelihood, we propose an easy-to-implement three-step method. RESULTS: We evaluate the performance of the proposed method through simulation studies and apply it to data from the Zurich Primary HIV Infection Study. We find that some key features of viral load during ART (e.g., viral decay rate) are significantly associated with important characteristics of viral rebound following ART interruption (e.g., viral set point). CONCLUSIONS: The proposed three-step method works well. We have shown that key features of viral decay during ART may be associated with important features of viral rebound following ART interruption

Effect of combined yoga and transcranial direct current stimulation intervention on working memory and mindfulness

Transcranial direct stimulation, a non-invasive neurostimulation technique for modulating cortical excitability, and yoga have both respectively been shown to positively affect cognition. While preliminary research has shown that combined transcranial direct stimulation and meditation may have synergistic effects on mood and cognition, this was the first study to explore the combination of transcranial direct stimulation and yoga. Twenty-two healthy volunteers with a regular yoga practice were randomized to receive either active transcranial direct stimulation (anodal left, cathodal right dorsolateral prefrontal cortex) followed by yoga intervention or sham transcranial direct stimulation followed by yoga intervention a double-blind, cross-over design over two separate intervention days. Outcome measures included working memory performance, measured with the n-back task and mindfulness state, measured with the Toronto Mindfulness Scale, and were conducted offline, with pre-post assessments. Twenty participants completed both days of the intervention. Active transcranial direct stimulation did not have a significant effect on working memory or levels of mindfulness. There was a significant placebo effect, with better performance on day 1 of the intervention, irrespective of whether participants received active or sham transcranial direct stimulation. There was no significant difference between active versus sham transcranial direct stimulation concerning working memory performance and mindfulness, which may be accounted by the small sample size, the transient nature of the intervention, the fact that yoga and transcranial direct stimulation concerning were not conducted simultaneously, and the specific site of stimulation