Literature DB >> 15807150

Statistical methods for HIV dynamic studies in AIDS clinical trials.

Hulin Wu1.   

Abstract

Studies of HIV dynamics in AIDS research are very important for understanding pathogenesis of HIV infection and for assessing the potency of antiviral therapies. Since the viral dynamic results from clinical data were first published by Ho et al. and Wei et al., the study of HIV-1 dynamics in vivo has drawn a great attention from AIDS clinicians and researchers. Although the important findings from HIV dynamic studies have been published in many prestigious scientific journals, statistical methods for estimating viral dynamic parameters have not been paid enough attention by HIV dynamic investigators. The estimation methods in many viral dynamic studies are very crude and inefficient. In this paper, we review the statistical methods and mathematical models for HIV dynamic data analysis developed in recent years. We also address some practical issues and share our experiences in the design and analysis of viral dynamic studies. Some principles and guidelines for the design and analysis of viral dynamic studies are provided. The methodologies reviewed in this paper are also applicable to studies of other viruses such as hepatitis B virus or hepatitis C virus. We also pose some challenging statistical problems in this area in order to stimulate further study by the statistical research community.

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Year:  2005        PMID: 15807150     DOI: 10.1191/0962280205sm390oa

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  21 in total

1.  The stochastic system approach for estimating dynamic treatments effect.

Authors:  Daniel Commenges; Anne Gégout-Petit
Journal:  Lifetime Data Anal       Date:  2015-02-11       Impact factor: 1.588

2.  Quantile regression for censored mixed-effects models with applications to HIV studies.

Authors:  Victor H Lachos; Ming-Hui Chen; Carlos A Abanto-Valle; Caio L N Azevedo
Journal:  Stat Interface       Date:  2015       Impact factor: 0.582

3.  Initial viral decay to assess the relative antiretroviral potency of protease inhibitor-sparing, nonnucleoside reverse transcriptase inhibitor-sparing, and nucleoside reverse transcriptase inhibitor-sparing regimens for first-line therapy of HIV infection.

Authors:  Richard H Haubrich; Sharon A Riddler; Heather Ribaudo; Gregory Direnzo; Karin L Klingman; Kevin W Garren; David L Butcher; James F Rooney; Diane V Havlir; John W Mellors
Journal:  AIDS       Date:  2011-11-28       Impact factor: 4.177

4.  Sieve Estimation of Constant and Time-Varying Coefficients in Nonlinear Ordinary Differential Equation Models by Considering Both Numerical Error and Measurement Error.

Authors:  Hongqi Xue; Hongyu Miao; Hulin Wu
Journal:  Ann Stat       Date:  2010-01-01       Impact factor: 4.028

5.  Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models.

Authors:  Hua Liang; Hulin Wu
Journal:  J Am Stat Assoc       Date:  2008-12-01       Impact factor: 5.033

6.  Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study.

Authors:  Mélanie Prague; Daniel Commenges; Jon Michael Gran; Bruno Ledergerber; Jim Young; Hansjakob Furrer; Rodolphe Thiébaut
Journal:  Biometrics       Date:  2016-07-26       Impact factor: 2.571

7.  Penalized Nonlinear Least Squares Estimation of Time-Varying Parameters in Ordinary Differential Equations.

Authors:  Jiguo Cao; Jianhua Z Huang; Hulin Wu
Journal:  J Comput Graph Stat       Date:  2012       Impact factor: 2.302

8.  HIV DYNAMICS AND NATURAL HISTORY STUDIES: JOINT MODELING WITH DOUBLY INTERVAL-CENSORED EVENT TIME AND INFREQUENT LONGITUDINAL DATA.

Authors:  Li Su; Joseph W Hogan
Journal:  Ann Appl Stat       Date:  2011-03-21       Impact factor: 2.083

9.  Parameter Estimation of Partial Differential Equation Models.

Authors:  Xiaolei Xun; Jiguo Cao; Bani Mallick; Raymond J Carroll; Arnab Maity
Journal:  J Am Stat Assoc       Date:  2013       Impact factor: 5.033

10.  Identifying significant covariates for anti-HIV treatment response: mechanism-based differential equation models and empirical semiparametric regression models.

Authors:  Yangxin Huang; Hua Liang; Hulin Wu
Journal:  Stat Med       Date:  2008-10-15       Impact factor: 2.373

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