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

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

Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues

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

Predictive Value of Blood Pressure, Heart Rate, and Blood Pressure/Heart Rate Ratio in a Chinese Subpopulation with Vasovagal Syncope

Objective: The head-up tilt test (HUTT) is widely used but is time-consuming and not cost-effective to evaluate patients with vasovagal syncope (VVS). The present study aims to verify the hypothesis that ambulatory blood pressure (BP) monitoring (ABPM) and the simplistic tilt test may be potential alternatives to the HUTT. Methods: The study consecutively enrolled 360 patients who underwent the HUTT to evaluate VVS. BP), heart rate (HR), and BP/HR ratios derived from ABPM and the simplistic tilt test were evaluated to predict the presence, pattern, and stage of syncope during the HUTT. Results: Mixed response was the commonest pattern, and syncope occurred frequently with infusion of isoproterenol at a rate of 3 μg/min. During the simplistic tilt test, the cardioinhibitory group had higher tilted BP/HR ratios than the vasodepressor group, while the vasodepressor group had a faster tilted HR and a larger HR difference than the cardioinhibitory group. The higher the BP/HR ratio in the tilted position, the higher the isoproterenol dosage needed to induce a positive response. During ABPM, BP/HR ratios were significantly higher in the cardioinhibitory group than in the vasodepressor group. The higher the ABPM-derived BP, the higher the dosage of isoproterenol needed to induce syncope. There were significant correlations in BP/HR ratios between ABPM and the supine position in the vasodepressor group, while significant correlation was found only for the diastolic BP/HR ratio between ABPM and the tilted position in the cardioinhibitory group. The mixed pattern shared correlative features of the other two patterns. Conclusion: ABPM and the simplistic tilt test might be used as promising alternatives to the HUTT in VVS evaluation in clinical settings

vFitness: a web-based computing tool for improving estimation of in vitro HIV-1 fitness experiments

<p>Abstract</p> <p>Background</p> <p>The replication rate (or fitness) between viral variants has been investigated <it>in vivo </it>and <it>in vitro </it>for human immunodeficiency virus (HIV). HIV fitness plays an important role in the development and persistence of drug resistance. The accurate estimation of viral fitness relies on complicated computations based on statistical methods. This calls for tools that are easy to access and intuitive to use for various experiments of viral fitness.</p> <p>Results</p> <p>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).</p> <p>Conclusions</p> <p>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 <url>http://bis.urmc.rochester.edu/vFitness/</url>.</p

Selection of number of dose levels and its robustness for binary response data

Müller & Schmitt (1990) have considered the question of how to choose the number of doses for estimating the median effective dose (ED50) when a probit dose-response curve is correctly assumed. However, they restricted their investigation to designs with doses symmetrical about the true ED50. In this paper, we investigate how the conclusions of Müller & Schmitt may change as the dose designs become slightly asymmetric about the true ED50. In addition, we address the question of the robustness of the number of doses chosen for an incorrectly assumed logistic model, when the dose designs are asymmetric about the assumed ED50. The underlying true dose-response curves considered here include the probit, cubic logistic and Aranda- Ordaz asymmetric models. The simulation results show that, for various underlying true dose-response curves and the uniform design density with doses spaced asymmetrically around the assumed ED50, the choice of as many doses as possible is almost optimal. This agrees with the results obtained for a correctly assumed probit or logistic dose-response curve when the dose designs are symmetric or slightly asymmetric about the ED50.

Robustness of interval estimation of the 90% effective dose: Bootstrap resampling and some large-sample parametric methods

Interval estimation of the x th effective dose (ED x ), where x is a prespecified percentage, has been the focus of interest of a number of recent studies, the majority of which have considered the case in which a logistic dose-response curve is correctly assumed. In this paper, we focus our attention upon the 90% effective dose (ED 90 ) and consider the situation in which the assumption of a logistic dose-response curve is incorrect. Specifically, we consider three classes of true model: the probit, the cubic logistic and the asymmetric Aranda-Ordaz models. We investigate the robustness of four large sample parametric methods of interval construction and four methods based upon bootstrap resampling.