Design of long-term HIV dynamic studies using semiparametric mixed-effects models.

Studies of HIV dynamics in AIDS research are very important in understanding the pathogenesis of HIV-1 infection and also in assessing the effectiveness of antiviral therapies. There are many AIDS clinical trials on HIV dynamics currently in development worldwide, giving rise to many design issues yet to be addressed. For example, most studies are focused on short-term viral dynamics and the existing models may not be applicable to describe long-term virologic response. In this paper, we use a simulation-based approach to study the designs of long-term viral dynamics under semiparametric nonlinear mixed-effects models. These models not only can preserve the meaningful interpretation of the short-term HIV dynamics, but also characterize the long-term virologic responses to antiretroviral (ARV) treatment. We investigate a number of feasible clinical protocol designs similar to those currently used in AIDS clinical trials. In particular, we evaluate whether earlier samplings can result in more useful information about the viral response trajectory; we also evaluate the effectiveness of two strategies: more frequent samplings per subject with fewer subjects versus fewer samplings per subject with more subjects while keeping the total number of samplings constant. The results of our investigation provide quantitative guidance for designing and selecting ARV therapy.

[1]  I. Marschner Design of HIV viral dynamics studies. , 1998, Statistics in medicine.

[2]  Kathryn Chaloner,et al.  DESIGN OF POPULATION STUDIES OF HIV DYNAMICS , 2005 .

[3]  Alan S. Perelson,et al.  A Novel Antiviral Intervention Results in More Accurate Assessment of Human Immunodeficiency Virus Type 1 Replication Dynamics and T-Cell Decay In Vivo , 2003, Journal of Virology.

[4]  Allan Donner,et al.  Design and Analysis of Cluster Randomization Trials in Health Research , 2001 .

[5]  V De Gruttola,et al.  Estimation of HIV dynamic parameters. , 1998, Statistics in medicine.

[6]  A. Ding,et al.  Assessing antiviral potency of anti-HIV therapies in vivo by comparing viral decay rates in viral dynamic models. , 2001, Biostatistics.

[7]  A A Ding,et al.  Relationships between antiviral treatment effects and biphasic viral decay rates in modeling HIV dynamics. , 1999, Mathematical biosciences.

[8]  P. Diggle,et al.  Analysis of Longitudinal Data , 2003 .

[9]  D O Scharfstein,et al.  The use of simulation and bootstrap in information-based group sequential studies. , 1998, Statistics in medicine.

[10]  Hulin Wu,et al.  Design of viral dynamic studies for efficiently assessing potency of anti-HIV therapies in AIDS Clinical Trials , 2002 .

[11]  Alan S. Perelson,et al.  Decay characteristics of HIV-1-infected compartments during combination therapy , 1997, Nature.

[12]  A. Perelson,et al.  HIV-1 Dynamics in Vivo: Virion Clearance Rate, Infected Cell Life-Span, and Viral Generation Time , 1996, Science.

[13]  Yangxin Huang,et al.  Bayesian Experimental Design for Long-Term Longitudinal HIV Dynamic Studies. , 2008, Journal of statistical planning and inference.

[14]  Hulin Wu,et al.  The study of long‐term HIV dynamics using semi‐parametric non‐linear mixed‐effects models , 2002, Statistics in medicine.

[15]  K. Chaloner,et al.  Bayesian Experimental Design: A Review , 1995 .

[16]  Victor DeGruttola,et al.  Dual vs single protease inhibitor therapy following antiretroviral treatment failure: a randomized trial. , 2002, JAMA.

[17]  A. Perelson,et al.  Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection , 1995, Nature.

[18]  M. Lederman,et al.  Immunologic responses associated with 12 weeks of combination antiretroviral therapy consisting of zidovudine, lamivudine, and ritonavir: results of AIDS Clinical Trials Group Protocol 315. , 1998, The Journal of infectious diseases.

[19]  Hulin Wu,et al.  Modeling Long-Term HIV Dynamics and Antiretroviral Response: Effects of Drug Potency, Pharmacokinetics, Adherence, and Drug Resistance , 2005, Journal of acquired immune deficiency syndromes.

[20]  W. Näther Optimum experimental designs , 1994 .

[21]  Hua Liang,et al.  Comparison of Linear, Nonlinear and Semiparametric Mixed‐effects Models for Estimating HIV Dynamic Parameters , 2004 .

[22]  H Wu,et al.  Population HIV‐1 Dynamics In Vivo: Applicable Models and Inferential Tools for Virological Data from AIDS Clinical Trials , 1999, Biometrics.

[23]  K. Chaloner,et al.  Optimum experimental designs for properties of a compartmental model. , 1993, Biometrics.

[24]  E. Walter,et al.  Robust experiment design via maximin optimization , 1988 .

[25]  L. Skovgaard NONLINEAR MODELS FOR REPEATED MEASUREMENT DATA. , 1996 .

[26]  Hulin Wu,et al.  Hierarchical Bayesian Methods for Estimation of Parameters in a Longitudinal HIV Dynamic System , 2006, Biometrics.

[27]  S. Hammer,et al.  A randomized trial of 2 different 4-drug antiretroviral regimens versus a 3-drug regimen, in advanced human immunodeficiency virus disease. , 2003, The Journal of infectious diseases.

[28]  D. Bates,et al.  Nonlinear mixed effects models for repeated measures data. , 1990, Biometrics.

[29]  Hulin Wu,et al.  Nonparametric regression methods for longitudinal data analysis , 2006 .

[30]  Kathryn Chaloner,et al.  Bayesian Experimental Design for Nonlinear Mixed‐Effects Models with Application to HIV Dynamics , 2004, Biometrics.

[31]  Hulin Wu,et al.  Comparison of Two Indinavir/Ritonavir Regimens in the Treatment of HIV-Infected Individuals , 2004, Journal of acquired immune deficiency syndromes.

[32]  Colin O. Wu,et al.  Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves , 2001, Biometrics.

[33]  Alain Mallet,et al.  Optimal design in random-effects regression models , 1997 .