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Search Results: 1 - 10 of 32545 matches for " Yangxin Huang "
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Modeling long-term longitudinal HIV dynamics with application to an AIDS clinical study
Yangxin Huang,Tao Lu
Statistics , 2009, DOI: 10.1214/08-AOAS192
Abstract: A virologic marker, the number of HIV RNA copies or viral load, is currently used to evaluate antiretroviral (ARV) therapies in AIDS clinical trials. This marker can be used to assess the ARV potency of therapies, but is easily affected by drug exposures, drug resistance and other factors during the long-term treatment evaluation process. HIV dynamic studies have significantly contributed to the understanding of HIV pathogenesis and ARV treatment strategies. However, the models of these studies are used to quantify short-term HIV dynamics ($<$ 1 month), and are not applicable to describe long-term virological response to ARV treatment due to the difficulty of establishing a relationship of antiviral response with multiple treatment factors such as drug exposure and drug susceptibility during long-term treatment. Long-term therapy with ARV agents in HIV-infected patients often results in failure to suppress the viral load. Pharmacokinetics (PK), drug resistance and imperfect adherence to prescribed antiviral drugs are important factors explaining the resurgence of virus. To better understand the factors responsible for the virological failure, this paper develops the mechanism-based nonlinear differential equation models for characterizing long-term viral dynamics with ARV therapy. The models directly incorporate drug concentration, adherence and drug susceptibility into a function of treatment efficacy and, hence, fully integrate virologic, PK, drug adherence and resistance from an AIDS clinical trial into the analysis. A Bayesian nonlinear mixed-effects modeling approach in conjunction with the rescaled version of dynamic differential equations is investigated to estimate dynamic parameters and make inference. In addition, the correlations of baseline factors with estimated dynamic parameters are explored and some biologically meaningful correlation results are presented. Further, the estimated dynamic parameters in patients with virologic success were compared to those in patients with virologic failure and significantly important findings were summarized. These results suggest that viral dynamic parameters may play an important role in understanding HIV pathogenesis, designing new treatment strategies for long-term care of AIDS patients.
Mixed-Effects Tobit Joint Models for Longitudinal Data with Skewness, Detection Limits, and Measurement Errors
Getachew A. Dagne,Yangxin Huang
Journal of Probability and Statistics , 2012, DOI: 10.1155/2012/614102
Abstract: Complex longitudinal data are commonly analyzed using nonlinear mixed-effects (NLME) models with a normal distribution. However, a departure from normality may lead to invalid inference and unreasonable parameter estimates. Some covariates may be measured with substantial errors, and the response observations may also be subjected to left-censoring due to a detection limit. Inferential procedures can be complicated dramatically when such data with asymmetric characteristics, left censoring, and measurement errors are analyzed. There is relatively little work concerning all of the three features simultaneously. In this paper, we jointly investigate a skew-t NLME Tobit model for response (with left censoring) process and a skew-t nonparametric mixed-effects model for covariate (with measurement errors) process under a Bayesian framework. A real data example is used to illustrate the proposed methods.
Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues
Lang Wu,Wei Liu,Grace Y. Yi,Yangxin Huang
Journal of Probability and Statistics , 2012, DOI: 10.1155/2012/640153
Abstract: In the past two decades, joint models of longitudinal and survival data have received much attention in the literature. These models are often desirable in the following situations: (i) survival models with measurement errors or missing data in time-dependent covariates, (ii) longitudinal models with informative dropouts, and (iii) a survival process and a longitudinal process are associated via latent variables. In these cases, separate inferences based on the longitudinal model and the survival model may lead to biased or inefficient results. In this paper, we provide a brief overview of joint models for longitudinal and survival data and commonly used methods, including the likelihood method and two-stage methods. 1. Introduction Longitudinal data and survival data frequently arise together in practice. For example, in many medical studies, we often collect patients’ information (e.g., blood pressures) repeatedly over time and we are also interested in the time to recovery or recurrence of a disease. Longitudinal data and survival data are often associated in some ways. The time to event may be associated with the longitudinal trajectories. Separate analyses of longitudinal data and survival data may lead to inefficient or biased results. Joint models of longitudinal and survival data, on the other hand, incorporate all information simultaneously and provide valid and efficient inferences. Figure 1 shows a longitudinal dataset in which CD4 cell counts are measured repeatedly over time in an AIDS study. Here, the time to event could be time to viral rebound, time to dropout, or time to death, depending on the research objectives. Data analysis can mainly focus on either the longitudinal data or the survival data or both. When the analysis focuses on longitudinal data, we often need to address informative dropouts since dropouts are very common in longitudinal studies. When the analysis focuses on survival data, we often need to incorporate time-dependent covariates such as CD4 since the times to event may be associated with the covariate trajectories. Sometimes, the main interest may lie in the association between the longitudinal process and survival process. In any of these cases, joint models are required to feature correlated longitudinal and survival data. Figure 1: CD4 measurements over time. (a) All subjects. (b) Five randomly selected subjects. Typically, joint models for longitudinal and survival data are required in the following situations: (i) survival models with measurement errors in time-dependent covariates; (ii) longitudinal models
Joint Models and Their Applications
Yangxin Huang,Lang Wu,Grace Y. Yi,Wenbin Lu
Journal of Probability and Statistics , 2012, DOI: 10.1155/2012/463506
Abstract:
Joint Models and Their Applications
Yangxin Huang,Lang Wu,Grace Y. Yi,Wenbin Lu
Journal of Probability and Statistics , 2012, DOI: 10.1155/2012/463506
Abstract:
vFitness: a web-based computing tool for improving estimation of in vitro HIV-1 fitness experiments
Jingming Ma, Carrie Dykes, Tao Wu, Yangxin Huang, Lisa Demeter, Hulin Wu
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-261
Abstract: Based on a mathematical model and several statistical methods (least-squares approach and measurement error models), a Web-based computing tool has been developed for improving estimation of virus fitness in growth competition assays of human immunodeficiency virus type 1 (HIV-1).Unlike the two-point calculation used in previous studies, the estimation here uses linear regression methods with all observed data in the competition experiment to more accurately estimate relative viral fitness parameters. The dilution factor is introduced for making the computational tool more flexible to accommodate various experimental conditions. This Web-based tool is implemented in C# language with Microsoft ASP.NET, and is publicly available on the Web at http://bis.urmc.rochester.edu/vFitness/ webcite.The replication rate (or fitness) between viral variants has been investigated in vivo [1,2] and in vitro [3-7] for human immunodeficiency virus (HIV). The lack of a consensus on how to measure fitness makes it difficult to determine if the replication capacity is important in disease progression. An accurate method to calculate fitness along with an easy to use tool will be valuable to virologists who study virus fitness.Although the importance of HIV fitness in disease progression is unknown, the fitness itself plays an important role in drug resistance [8]. In order to develop a better understanding of viral fitness, Marée et al. proposed a mathematical model to describe the dynamics of viral competition between a wild-type virus and a mutant virus, and presented a formula to calculate the relative fitness 1+s based on data collected from two time points during the course of the experiment [6]. Here, s is the selection coefficient [9]. If there are more than two time points, investigators must choose a pair of time points for the calculation of relative fitness, and the formula does not provide a way to obtain a more accurate estimation over all the observed data. Bonhoeffer et a
A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates
Yangxin Huang,Hulin Wu,Jeanne Holden-Wiltse,Edward P. Acosta
Statistics , 2011, DOI: 10.1214/10-AOAS376
Abstract: HIV dynamic studies have contributed significantly to the understanding of HIV pathogenesis and antiviral treatment strategies for AIDS patients. Establishing the relationship of virologic responses with clinical factors and covariates during long-term antiretroviral (ARV) therapy is important to the development of effective treatments. Medication adherence is an important predictor of the effectiveness of ARV treatment, but an appropriate determinant of adherence rate based on medication event monitoring system (MEMS) data is critical to predict virologic outcomes. The primary objective of this paper is to investigate the effects of a number of summary determinants of MEMS adherence rates on virologic response measured repeatedly over time in HIV-infected patients. We developed a mechanism-based differential equation model with consideration of drug adherence, interacted by virus susceptibility to drug and baseline characteristics, to characterize the long-term virologic responses after initiation of therapy. This model fully integrates viral load, MEMS adherence, drug resistance and baseline covariates into the data analysis. In this study we employed the proposed model and associated Bayesian nonlinear mixed-effects modeling approach to assess how to efficiently use the MEMS adherence data for prediction of virologic response, and to evaluate the predicting power of each summary metric of the MEMS adherence rates.
Association between MGMT Promoter Methylation and Non-Small Cell Lung Cancer: A Meta-Analysis
Changmei Gu, Jiachun Lu, Tianpen Cui, Cheng Lu, Hao Shi, Wenmao Xu, Xueli Yuan, Xiaobo Yang, Yangxin Huang, Meixia Lu
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0072633
Abstract: Background O6-methylguanine-DNA methyltransferase (MGMT) is one of most important DNA repair enzyme against common carcinogens such as alkylate and tobacco. Aberrant promoter methylation of the gene is frequently observed in non-small cell lung cancer (NSCLC). However, the importance of epigenetic inactivation of the gene in NSCLC published in the literature showed inconsistence. We quantified the association between MGMT promoter methylation and NSCLC using a meta-analysis method. Methods We systematically reviewed studies of MGMT promoter methylation and NSCLC in PubMed, EMBASE, Ovid, ISI Web of Science, Elsevier and CNKI databases and quantified the association between MGMT promoter methylation and NSCLC using meta-analysis method. Odds ratio (OR) and corresponding 95% confidence interval (CI) were calculated to evaluate the strength of association. Potential sources of heterogeneity were assessed by subgroup analysis and meta-regression. Results A total of 18 studies from 2001 to 2011, with 1, 160 tumor tissues and 970 controls, were involved in the meta-analysis. The frequencies of MGMT promote methylation ranged from 1.5% to 70.0% (median, 26.1%) in NSCLC tissue and 0.0% to 55.0% (median, 2.4%) in non-cancerous control, respectively. The summary of OR was 4.43 (95% CI: 2.85, 6.89) in the random-effects model. With stratification by potential source of heterogeneity, the OR was 20.45 (95% CI: 5.83, 71.73) in heterogeneous control subgroup, while it was 4.16 (95% CI: 3.02, 5.72) in the autologous control subgroup. The OR was 5.31 (95% CI: 3.00, 9.41) in MSP subgroup and 3.06 (95% CI: 1.75, 5.33) in Q-MSP subgroup. Conclusion This meta-analysis identified a strong association between methylation of MGMT gene and NSCLC. Prospective studies should be required to confirm the results in the future.
Association between RASSF1A Promoter Methylation and Ovarian Cancer: A Meta-Analysis
Hao Shi, Ya Li, Xiaozhong Wang, Cheng Lu, Lilan Yang, Changmei Gu, Jiaqiang Xiong, Yangxin Huang, Shixuan Wang, Meixia Lu
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0076787
Abstract: Background The RAS association domain family protein 1a gene (RASSF1A) is one of the tumor suppressor genes (TSG). Inactivation of RASSF1A is critical to the pathogenesis of cancer. Aberrant TSG methylation was considered an important epigenetic silencing mechanism in the progression of ovarian cancer. A number of studies have discussed association between RASSF1A promoter methylation and ovarian cancer. However, they were mostly based on a small number of samples and showed inconsist results, Therefore, we conducted a meta-analysis to better identify the association. Methods Eligible studies were identified by searching the PubMed, EMBASE, Web of Science, and CNKI databases using a systematic searching strategy. We pooled the odds ratio (ORs) from individual studies using a fixed-effects model. We performed heterogeneity and publication bias analysis simultaneously. Results Thirteen studies, with 763 ovarian cancer patients and 438 controls were included in the meta-analysis. The frequencies of RASSF1A promoter methylation ranged from 30% to 58% (median is 48%) in the cancer group and 0 to 21% (median is 0) in the control group. The frequencies of RASSF1A promoter methylation in the cancer group were significantly higher than those in the control group. The pooled odds ratio was 11.17 (95% CI = 7.51–16.61) in the cancer group versus the corresponding control group under the fixed-effects model. Conclusion The results suggested that RASSF1A promoter methylation had a strong association with ovarian cancer.
A new role for T cells in dampening innate inflammatory responses
Hong Tang,YangXin Fu
Science China Life Sciences , 2010, DOI: 10.1007/s11427-010-0040-5
Abstract:
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