16,218 research outputs found
Estimation of constant and time-varying dynamic parameters of HIV infection in a nonlinear differential equation model
Modeling viral dynamics in HIV/AIDS studies has resulted in a deep
understanding of pathogenesis of HIV infection from which novel antiviral
treatment guidance and strategies have been derived. Viral dynamics models
based on nonlinear differential equations have been proposed and well developed
over the past few decades. However, it is quite challenging to use experimental
or clinical data to estimate the unknown parameters (both constant and
time-varying parameters) in complex nonlinear differential equation models.
Therefore, investigators usually fix some parameter values, from the literature
or by experience, to obtain only parameter estimates of interest from clinical
or experimental data. However, when such prior information is not available, it
is desirable to determine all the parameter estimates from data. In this paper
we intend to combine the newly developed approaches, a multi-stage
smoothing-based (MSSB) method and the spline-enhanced nonlinear least squares
(SNLS) approach, to estimate all HIV viral dynamic parameters in a nonlinear
differential equation model. In particular, to the best of our knowledge, this
is the first attempt to propose a comparatively thorough procedure, accounting
for both efficiency and accuracy, to rigorously estimate all key kinetic
parameters in a nonlinear differential equation model of HIV dynamics from
clinical data. These parameters include the proliferation rate and death rate
of uninfected HIV-targeted cells, the average number of virions produced by an
infected cell, and the infection rate which is related to the antiviral
treatment effect and is time-varying. To validate the estimation methods, we
verified the identifiability of the HIV viral dynamic model and performed
simulation studies.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS290 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates
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.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS376 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Sieve estimation of constant and time-varying coefficients in nonlinear ordinary differential equation models by considering both numerical error and measurement error
This article considers estimation of constant and time-varying coefficients
in nonlinear ordinary differential equation (ODE) models where analytic
closed-form solutions are not available. The numerical solution-based nonlinear
least squares (NLS) estimator is investigated in this study. A numerical
algorithm such as the Runge--Kutta method is used to approximate the ODE
solution. The asymptotic properties are established for the proposed estimators
considering both numerical error and measurement error. The B-spline is used to
approximate the time-varying coefficients, and the corresponding asymptotic
theories in this case are investigated under the framework of the sieve
approach. Our results show that if the maximum step size of the -order
numerical algorithm goes to zero at a rate faster than , the
numerical error is negligible compared to the measurement error. This result
provides a theoretical guidance in selection of the step size for numerical
evaluations of ODEs. Moreover, we have shown that the numerical solution-based
NLS estimator and the sieve NLS estimator are strongly consistent. The sieve
estimator of constant parameters is asymptotically normal with the same
asymptotic co-variance as that of the case where the true ODE solution is
exactly known, while the estimator of the time-varying parameter has the
optimal convergence rate under some regularity conditions. The theoretical
results are also developed for the case when the step size of the ODE numerical
solver does not go to zero fast enough or the numerical error is comparable to
the measurement error. We illustrate our approach with both simulation studies
and clinical data on HIV viral dynamics.Comment: Published in at http://dx.doi.org/10.1214/09-AOS784 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Numerical optimal control for HIV prevention with dynamic budget allocation
This paper is about numerical control of HIV propagation. The contribution of
the paper is threefold: first, a novel model of HIV propagation is proposed;
second, the methods from numerical optimal control are successfully applied to
the developed model to compute optimal control profiles; finally, the computed
results are applied to the real problem yielding important and practically
relevant results.Comment: Submitted pape
Modeling long-term longitudinal HIV dynamics with application to an AIDS clinical study
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.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS192 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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