Estimating correlation by using a general linear mixed model: evaluation of the relationship between the concentration of HIV-1 RNA in blood and semen

A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data

In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 × $\times$ 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi et al. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a2 × 2 $2\times 2$ crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modeling framework.

Multivariate linear mixed models with censored and nonignorable missing outcomes, with application to AIDS studies

The analysis of multivariate longitudinal data could encounter some complications due to censorship induced by detection limits of the assay and nonresponse occurring when participants missed scheduled visits intermittently or discontinued participation. This paper establishes a generalization of the multivariate linear mixed model that can accommodate censored responses and nonignorable missing outcomes simultaneously. To account for the nonignorable missingness, the selection approach which decomposes the joint distribution as a marginal distribution for the primary outcome variables and a model describing the missing process conditional on the hypothetical complete data is used. A computationally feasible Monte Carlo expectation conditional maximization algorithm is developed for parameter estimation with the maximum likelihood (ML) method. Furthermore, a general information-based approach is presented to assess the variability of ML estimators. The techniques for the prediction of censored responses and imputation of missing outcomes are also discussed. The methodology is motivated and exemplified by a real dataset concerning HIV-AIDS clinical trials. A simulation study is conducted to examine the performance of the proposed method compared with other traditional approaches.

Dynamics of cytokines, immune cell counts and disease severity in patients with community-acquired pneumonia - Unravelling potential causal relationships

BACKGROUND

Community acquired pneumonia (CAP) is a severe and often rapidly deteriorating disease. To better understand its dynamics and potential causal relationships, we analyzed time series data of cytokines, blood and clinical parameters in hospitalized CAP patients.

METHODS

Time series data of 10 circulating cytokines, blood counts and clinical parameters were related to baseline characteristics of 403 CAP patients using univariate mixed models. Bivariate mixed models were applied to analyze correlations between the time series. To identify potential causal relationships, we inferred cross-lagged relationships between pairs of parameters using latent curve models with structured residuals.

RESULTS

IL-6 levels decreased faster over time in younger patients (P_adj = 0.06). IL-8, VCAM-1, and IL-6 correlated strongly with disease severity as assessed by the sequential organ failure assessment (SOFA) score (r = 0.49, 0.48, 0.46, respectively; all P_adj < 0.001). IL-6 and bilirubin correlated with respect to their mean levels and slopes over time (r = 0.36 and r = 0.46, respectively; P_adj < 0.001). A number of potential causal relationships were identified, e.g., a negative effect of ICAM-1 on MCP-1, or a positive effect of the level of creatinine on the subsequent VCAM-1 concentration (P < 0.001).

CONCLUSIONS

These results suggest that IL-6 trajectories of CAP patients are associated with age and run parallel to bilirubin levels. The time series analysis also unraveled directed, potentially causal relationships between cytokines, blood parameters and clinical outcomes. This will facilitate the development of mechanistic models of CAP, and with it, improvements in treatment or surveillance strategies for this disease.

TRIAL REGISTRATION

clinicaltrials.gov NCT02782013, May 25, 2016, retrospectively registered.

The Effect of Molecular Weight on Passage of Proteins Through the Blood-Aqueous Barrier

Purpose

To determine the effect of molecular weight (MW) on the concentration of plasma-derived proteins in aqueous humor and to estimate the plasma-derived and eye-derived fractions for each protein.

Methods

Aqueous humor and plasma samples were obtained during cataract surgery on an institutional review board-approved protocol. Protein concentrations were determined by ELISA and quantitative antibody microarrays. A total of 93 proteins were studied, with most proteins analyzed using 27 to 116 aqueous and 6 to 30 plasma samples.

Results

Plasma proteins without evidence of intraocular expression by sequence tags were used to fit a logarithmic model relating aqueous-plasma ratio (AH:PL) to MW. The log(AH:PL) appears to be well predicted by the log(MW) (P < 0.0001), with smaller proteins such as cystatin C (13 kDa) having a higher AH:PL (1:6) than larger proteins such as albumin (66 kDa, 1:300) and complement component 5 (188 kDa, 1:2500). The logarithmic model was used to calculate the eye-derived intraocular fraction (IOF) for each protein. Based on the IOF, 66 proteins could be categorized as plasma-derived (IOF<20), whereas 10 proteins were primarily derived from eye tissue (IOF >80), and 17 proteins had contribution from both plasma and eye tissue (IOF 20-80).

Conclusions

Protein concentration of plasma-derived proteins in aqueous is nonlinearly dependent on MW in favor of smaller proteins. Our study demonstrates that for proper interpretation of results, proteomic studies evaluating changes in aqueous humor protein levels should take into account the plasma and eye-derived fractions.

Viral Kinetics in Semen With Different Antiretroviral Families in Treatment-Naive Human Immunodeficiency Virus-Infected Patients: A Randomized Trial

Background

There are several regimens for starting antiretroviral treatment, but it remains unknown whether either of them is more advantageous regarding the time course and magnitude of human immunodeficiency virus (HIV) RNA decay in semen.

Objective

To evaluate the differential effect of different antiretroviral drug families on viral kinetics in seminal plasma (SP) of treatment-naive HIV-infected patients.

Methods

Phase II, randomized, open-label study in which participants were randomized 1:1:1 to receive tenofovir-disoproxil fumarate (DF) plus emtricitabine, and either cobicistat-boosted elvitegravir (EVGcobi), rilpivirine (RPV), or ritonavir-boosted darunavir (DRVrtv). The primary endpoint was the proportion of participants with undetectable HIV-RNA in SP at week 12. HIV type 1 (HIV-1) RNA was measured in paired SP and blood plasma (BP) at baseline and after 1, 2, 4, 6, 8, 12, 18, and 24 weeks. Elvitegravir (EVG), RPV, and darunavir (DRV) concentrations were quantified by the liquid chromatography-tandem mass spectrometry method.

Results

In SP, the HIV-RNA decay rate with RPV was as fast as with EVGcobi; by week 12, all participants in the RPV and the EVGcobi groups reached an undetectable viral load but only 58.3% in the DRVrtv arm (P = .003). The highest SP/BP drug concentration ratio was for EVG (0.43), followed-up by RPV (0.19), and DRV (0.10). For both EVG and RPV, the SP concentrations exceeded >2-fold the protein binding-adjusted EC90 for wild-type HIV-1; for DRV, only 33.7% of the SP showed concentrations above the protein binding-adjusted EC90.

Conclusions

In SP, both RPV and EVGcobi, associated to tenofovir-DF and emtricitabine, behave similarly and achieve an undetectable viral load much faster than DRVrtv.

Registration

European Medical Agency (No. EudraCT: 2014-001348-39).

Bivariate longitudinal data analysis: a case of hypertensive patients at Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia

Objective

Longitudinal data are often collected to study the evolution of biomedical markers. The study of the joint evolution of response variables concerning hypertension over time was the aim of this paper. A hospital based retrospective data were collected from September 2014 to August 2015 to identify factors that affect hypertensive. The joint mixed effect model with unstructured covariance was fitted. A total of 172 patients screened for antihypertensive drugs treated were longitudinally considered from Felege Hiwot referral.

Results

The joint mixed effect model with unstructured covariance (AIC: 12,236.9 with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \chi_{12}^{2} $$\end{document}χ122 = 1007.8, P < 10⁻⁴) was significantly best fit to the data. The correlation between the evolutions of DBP and SBP was 0.429 and the evolution of the association between responses over-time was found 0.257. Among all covariates included in joint-mixed-effect-models, sex, residence, related disease and time were statistically significant on evolution of systolic and diastolic blood pressure. The joint modeling of longitudinal bivariate responses is necessary to explore the association between paired response variables like systolic and diastolic blood pressure. Fitting joint model with modern computing method is recommended to address questions for association of the evolutions with better accuracy.

HIV-1 is undetectable in preejaculatory secretions from HIV-1-infected men on suppressive HAART

OBJECTIVE

Preejaculatory fluid, a viscous, lubricating secretion expressed by penile urethral glands during sexual excitement, may play a role in the sexual transmission of HIV-1. The urethra has been shown to be an important site of HIV infection in men and male macaques, and preejaculatory fluid and urethral swabs from HIV-1-infected men often contain HIV. Recent studies have shown that HAART reduces but does not eliminate seminal HIV shedding in infected men, and that the penile urethra remains a site of persistent simian immunodeficiency virus infection in HAART-treated macaques. The objective of this study was to determine whether HIV-infected men on stable HAART continue to shed HIV into preejaculatory secretions.

DESIGN

Single-center prospective study.

METHODS

Sixty HIV-infected men on HAART were recruited to provide preejaculatory fluid, semen, and blood for HIV RNA quantification by reverse transcription-PCR.

RESULTS

Eight men had detectable HIV in blood; of these four had HIV in semen (range: 40-96 000 copies/ml), and one had HIV in preejaculate (2400 copies/sample). Fifty-two men had undetectable HIV RNA in blood; of these 10 (19.2%) had HIV RNA in semen (range: 59-800 copies/ml) whereas none (0%) had HIV RNA in preejaculate (P = 0.004).

CONCLUSIONS

This study documents for the first time high levels of HIV RNA in preejaculate fluid. However, none of the men on stable HAART with undetectable blood viral load had HIV RNA in preejaculate, even though many had detectable HIV in semen. The urethral glands do not appear to be a principal source of HIV in men on suppressive HAART.

Mixed models approaches for joint modeling of different types of responses

In many biomedical studies, one jointly collects longitudinal continuous, binary, and survival outcomes, possibly with some observations missing. Random-effects models, sometimes called shared-parameter models or frailty models, received a lot of attention. In such models, the corresponding variance components can be employed to capture the association between the various sequences. In some cases, random effects are considered common to various sequences, perhaps up to a scaling factor; in others, there are different but correlated random effects. Even though a variety of data types has been considered in the literature, less attention has been devoted to ordinal data. For univariate longitudinal or hierarchical data, the proportional odds mixed model (POMM) is an instance of the generalized linear mixed model (GLMM; Breslow and Clayton, 1993). Ordinal data are conveniently replaced by a parsimonious set of dummies, which in the longitudinal setting leads to a repeated set of dummies. When ordinal longitudinal data are part of a joint model, the complexity increases further. This is the setting considered in this paper. We formulate a random-effects based model that, in addition, allows for overdispersion. Using two case studies, it is shown that the combination of random effects to capture association with further correction for overdispersion can improve the model's fit considerably and that the resulting models allow to answer research questions that could not be addressed otherwise. Parameters can be estimated in a fairly straightforward way, using the SAS procedure NLMIXED.

Defining the interaction of HIV-1 with the mucosal barriers of the female reproductive tract

Worldwide, HIV-1 infects millions of people annually, the majority of whom are women. To establish infection in the female reproductive tract (FRT), HIV-1 in male ejaculate must overcome numerous innate and adaptive immune factors, traverse the genital epithelium, and establish infection in underlying CD4(+) target cells. How the virus achieves this remains poorly defined. By utilizing a new technique, we define how HIV-1 interacts with different tissues of the FRT using human cervical explants and in vivo exposure in the rhesus macaque vaginal transmission model. Despite previous claims of the squamous epithelium being an efficient barrier to virus entry, we reveal that HIV-1 can penetrate both intact columnar and squamous epithelial barriers to depths where the virus can encounter potential target cells. In the squamous epithelium, we identify virus entry occurring through diffusive percolation, penetrating areas where cell junctions are absent. In the columnar epithelium, we illustrate that virus does not transverse barriers as well as previously thought due to mucus impediment. We also show a statistically significant correlation between the viral load of inocula and the ability of HIV-1 to pervade the squamous barrier. Overall, our results suggest a diffusive percolation mechanism for the initial events of HIV-1 entry. With these data, we also mathematically extrapolate the number of HIV-1 particles that penetrate the mucosa per coital act, providing a biological description of the mechanism for HIV-1 transmission during the acute and chronic stages of infection.

Multivariate t linear mixed models for irregularly observed multiple repeated measures with missing outcomes

Missing outcomes or irregularly timed multivariate longitudinal data frequently occur in clinical trials or biomedical studies. The multivariate t linear mixed model (MtLMM) has been shown to be a robust approach to modeling multioutcome continuous repeated measures in the presence of outliers or heavy-tailed noises. This paper presents a framework for fitting the MtLMM with an arbitrary missing data pattern embodied within multiple outcome variables recorded at irregular occasions. To address the serial correlation among the within-subject errors, a damped exponential correlation structure is considered in the model. Under the missing at random mechanism, an efficient alternating expectation-conditional maximization (AECM) algorithm is used to carry out estimation of parameters and imputation of missing values. The techniques for the estimation of random effects and the prediction of future responses are also investigated. Applications to an HIV-AIDS study and a pregnancy study involving analysis of multivariate longitudinal data with missing outcomes as well as a simulation study have highlighted the superiority of MtLMMs on the provision of more adequate estimation, imputation and prediction performances.

Order-restricted inference for multivariate longitudinal data with applications to the natural history of hearing loss

Multivariate outcomes are often measured longitudinally. For example, in hearing loss studies, hearing thresholds for each subject are measured repeatedly over time at several frequencies. Thus, each patient is associated with a multivariate longitudinal outcome. The multivariate mixed-effects model is a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, it is known that hearing thresholds, at every frequency, increase with age. Moreover, this age-related threshold elevation is monotone in frequency, that is, the higher the frequency, the higher, on average, is the rate of threshold elevation. This means that there is a natural ordering among the different frequencies in the rate of hearing loss. In practice, this amounts to imposing a set of constraints on the different frequencies' regression coefficients modeling the mean effect of time and age at entry to the study on hearing thresholds. The aforementioned constraints should be accounted for in the analysis. The result is a multivariate longitudinal model with restricted parameters. We propose estimation and testing procedures for such models. We show that ignoring the constraints may lead to misleading inferences regarding the direction and the magnitude of various effects. Moreover, simulations show that incorporating the constraints substantially improves the mean squared error of the estimates and the power of the tests. We used this methodology to analyze a real hearing loss study.

Cyclic changes in the level of the innate immune molecule, surfactant protein-a, and cytokines in vaginal fluid

PROBLEM

Our knowledge of the innate host defenses in the vagina, a site where these defenses are essential to protecting the host upper reproductive tract from invasion by pathogens, is as yet rudimentary. Specifically, little is known about the pattern-recognition component of vaginal innate immunity, the relationship of pattern-recognition molecules to known cytokine levels, and the role of gonadal hormones in their regulation.

METHOD OF STUDY

We measured levels of Surfactant Protein-A (SP-A), a prototypic innate pattern-recognition protein, in vaginal fluid (VF) and correlated them with levels of IL-1β and IL-8, two cytokines known to be present in VF. Assays were carried out on VF collected over three consecutive cycles from ten healthy naturally cycling women who were sampled at three specific time points in the menstrual cycle. The three time points were chosen to enable correlation with distinct hormonal states.

RESULTS

Both SP-A and cytokines levels were highest 5-6 days after menses (P < 0.05) and were significantly lower at ovulation and mid-luteal phase.

CONCLUSION

Surfactant Protein-A, like other host defense molecules in the reproductive tract, appears to be regulated by gonadal hormones.

Combining structural and functional measurements to improve estimates of rates of glaucomatous progression

PURPOSE

To evaluate whether a combination of topographic optic disc measurements and visual field data may improve the estimation of rates of structural and functional progression in glaucoma and the prediction of future outcomes in the disease.

DESIGN

Observational cohort study.

METHODS

The study included 242 eyes of 179 glaucoma patients followed for an average of 6.4 ± 1.1 years. Subjects were longitudinally monitored with standard automated perimetry (SAP) and confocal scanning laser ophthalmoscopy (CSLO). Slopes of functional and structural change over time were evaluated by the parameters mean deviation (MD) and rim area, respectively. For each eye, the follow-up time was divided into 2 equal periods: the first half was used to obtain the slopes of change and the second period was used to test the predictions. Slopes of change were calculated using 2 methods, the conventional approach of ordinary least squares linear regression and a Bayesian joint regression model integrating structural and functional information. The mean square error (MSE) of the predictions was used to compare the predictive performance of the different methods.

RESULTS

Bayesian slopes were more accurate than those obtained by the ordinary least squares method in predicting future MD (MSE: 5.13 vs 11.2, respectively; P < .001) and rim area values (MSE: 0.016 vs 0.027, respectively; P < .01).

CONCLUSION

A Bayesian joint regression model combining structure and function resulted in more accurate and precise estimates of slopes of change compared to the conventional method of ordinary least squares linear regression.

The analysis of multivariate longitudinal data: a review

Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions can only be answered in a joint analysis of all of them. In this article, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details.

Combining structural and functional measurements to improve detection of glaucoma progression using Bayesian hierarchical models

PURPOSE

To present and evaluate a new methodology for combining longitudinal information from structural and functional tests to improve detection of glaucoma progression and estimation of rates of change.

METHODS

This observational cohort study included 434 eyes of 257 participants observed for an average of 4.2 ± 1.1 years and recruited from the Diagnostic Innovations in Glaucoma Study (DIGS). The subjects were examined annually with standard automated perimetry, optic disc stereophotographs, and scanning laser polarimetry with enhanced corneal compensation. Rates of change over time were measured using the visual field index (VFI) and average retinal nerve fiber layer thickness (TSNIT average). A bayesian hierarchical model was built to integrate information from the longitudinal measures and classify individual eyes as progressing or not. Estimates of sensitivity and specificity of the bayesian method were compared with those obtained by the conventional approach of ordinary least-squares (OLS) regression.

RESULTS

The bayesian method identified a significantly higher proportion of the 405 glaucomatous and suspect eyes as having progressed when compared with the OLS method (22.7% vs. 12.8%; P < 0.001), while having the same specificity of 100% in 29 healthy eyes. In addition, the bayesian method identified a significantly higher proportion of eyes with progression by optic disc stereophotographs compared with the OLS method (74% vs. 37%; P = 0.001).

CONCLUSIONS

A bayesian hierarchical modeling approach for combining functional and structural tests performed significantly better than the OLS method for detection of glaucoma progression. (ClinicalTrials.gov number, NCT00221897.).

Treatment to prevent transmission of HIV-1

Antiretroviral therapy (ART) has the potential to prevent human immunodeficiency virus (HIV) transmission by reducing the concentration of HIV in blood and genital secretions. Indeed, mathematical models with favorable assumptions suggest the potential of ART to stop the spread of HIV infection. Empirical results from ecological and population-based studies and from several short-term observational studies involving HIV status-discordant heterosexual couples suggest that ART reduces the rate of HIV transmission. A multinational, randomized, controlled trial (National Institutes of Health HPTN052) examining the reliability and durability of ART as prevention of transmission in HIV status-discordant couples is under way. The latter and other studies also consider sexual risk-taking behavior and transmission of HIV-resistant variants when ART is used as prevention. Early HIV detection and treatment (ie, test and treat) are being considered as an important prevention strategy. In this article, we review the data supporting the use of ART to prevent HIV transmission and critically examine the public health implications of this strategy.

Joint analysis of multiple longitudinal outcomes: application of a latent class model

We address the problem of joint analysis of more than one series of longitudinal measurements. The typical way of approaching this problem is as a joint mixed effects model for the two outcomes. Apart from the large number of parameters needed to specify such a model, perhaps the biggest drawback of this approach is the difficulty in interpreting the results of the model, particularly when the main interest is in the relation between the two longitudinal outcomes. Here we propose an alternative approach to this problem. We use a latent class joint model for the longitudinal outcomes in order to reduce the dimensionality of the problem. We then use a two-stage estimation procedure to estimate the parameters in this model. In the first stage, the latent classes, their probabilities and the mean and covariance structure are estimated based on the longitudinal data of the first outcome. In the second stage, we study the relation between the latent classes and patient characteristics and the other outcome(s). We apply the method to data from 195 consecutive lung cancer patients in two outpatient clinics of lung diseases in The Hague, and we study the relation between denial and longitudinal health measures. Our approach clearly revealed an interesting phenomenon: although no difference between classes could be detected for objective measures of health, patients in classes representing higher levels of denial consistently scored significantly higher in subjective measures of health.

Understanding transmitted HIV resistance through the experience in the USA

Transmitted drug resistance is an emerging phenomenon with important clinical and public health implications. It has been reported in 3.4% to 26% of HIV-infected persons in the USA. Most cases affect non-nucleoside reverse transcriptase inhibitors or nucleos(t)ide reverse transcriptase inhibitors. Transmitted protease inhibitor or multi-class resistance is uncommon, occurring in <5% of cases. The genital tract may function as a reservoir of transmissible drug-resistant variants or a site for low-level viral replication at a time plasma HIV is suppressed. Transmitted drug-resistant HIV variants, including those that exist in very low titers (minority populations), are associated with suboptimal virologic response to initial antiretroviral therapy. Baseline resistance testing, preferably genotype, appears to be cost-effective and is recommended for all treatment-naïve patients in the USA, although prospective trials have not been performed. It appears transmitted drug resistance is still relatively low in developing countries, but there is a dearth of information.

Human immunodeficiency virus viral load in blood plasma and semen: review and implications of empirical findings

The majority of human immunodeficiency virus (HIV) infections in the world are sexually transmitted and quantities of HIV in genital fluids are an important transmission risk-determining factor. Estimating men's sexual HIV infectiousness from blood viral load hinges on the association between HIV in blood plasma (BPVL) and semen viral load (SVL). This article reviews research on the association between BPVL and SVL as reported in 19 empirical studies (N = 1226). Findings yielded a mean correlation between BPVL and SVL of 0.45 (SD = 0.20, median = 0.45, range = 0.07-.64). SVL was generally lower than BPVL, but this pattern was variable across studies. Co-occurring sexually transmitted infections (urethritis), nonsuppressive HIV treatments, and drug resistance account for the variability in observed correlations. HIV disease progression does not reliably influence the association between BPVL and SVL. Research is needed to determine the degree to which BPVL as well as SVL predict HIV transmission.

Random-effects models for multivariate repeated measures

Mixed models are widely used for the analysis of one repeatedly measured outcome. If more than one outcome is present, a mixed model can be used for each one. These separate models can be tied together into a multivariate mixed model by specifying a joint distribution for their random effects. This strategy has been used for joining multivariate longitudinal profiles or other types of multivariate repeated data. However, computational problems are likely to occur when the number of outcomes increases. A pairwise modeling approach, in which all possible bivariate mixed models are fitted and where inference follows from pseudo-likelihood arguments, has been proposed to circumvent the dimensional limitations in multivariate mixed models. An analysis on 22-variate longitudinal measurements of hearing thresholds illustrates the performance of the pairwise approach in the context of multivariate linear mixed models. For generalized linear mixed models, a data set containing repeated measurements of seven aspects of psycho-cognitive functioning will be analyzed.

Bivariate random effect model using skew-normal distribution with application to HIV-RNA

Correlated data arise in a longitudinal studies from epidemiological and clinical research. Random effects models are commonly used to model correlated data. Mostly in the longitudinal data setting we assume that the random effects and within subject errors are normally distributed. However, the normality assumption may not always give robust results, particularly if the data exhibit skewness. In this paper, we develop a Bayesian approach to bivariate mixed model and relax the normality assumption by using a multivariate skew-normal distribution. Specifically, we compare various potential models and illustrate the procedure using a real data set from HIV study.

Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles

A mixed model is a flexible tool for joint modeling purposes, especially when the gathered data are unbalanced. However, computational problems due to the dimension of the joint covariance matrix of the random effects arise as soon as the number of outcomes and/or the number of used random effects per outcome increases. We propose a pairwise approach in which all possible bivariate models are fitted, and where inference follows from pseudo-likelihood arguments. The approach is applicable for linear, generalized linear, and nonlinear mixed models, or for combinations of these. The methodology will be illustrated for linear mixed models in the analysis of 22-dimensional, highly unbalanced, longitudinal profiles of hearing thresholds.

Behavior change and health-related interventions for heterosexual risk reduction among drug users

Prevention of heterosexual transmission of HIV between and from drug users is important for controlling the local and global HIV heterosexual epidemic. Sex risk reduction interventions and health-related interventions are important for reducing the sex risk behaviors of drug users. Sex risk reduction interventions address individual-level, peer-level, and structural-level determinants of risk reduction. Health-related interventions include HIV counseling and testing, prevention and treatment of sexually transmitted diseases, and delivery of highly active antiretroviral therapy. It is important to adapt effective interventions implemented in resource-rich countries to the realities of the resource-constrained settings and to address relevant contextual factors.

The role of sexually transmitted diseases in HIV transmission

More than 42 million people worldwide are now infected with HIV, in spite of sustained prevention activities. Although the spread of HIV has been primarily sexual, epidemiological studies have indicated that the efficiency of the spread of HIV is poor, perhaps as infrequently as 1 in every 1,000 episodes of sexual intercourse. However, sexually transmitted diseases (STDs) that cause ulcers or inflammation greatly increase the efficiency of HIV transmission--by increasing both the infectiousness of, and the susceptibility to HIV infection. STDs might be particularly important in the early stages of a localized HIV epidemic, when people with risky sexual behaviour are most likely to become infected. In China, eastern Europe and Russia, there has been a remarkable increase in the incidence of STDs in recent years, and this is reflected in the rapid increase in the spread of HIV in these areas. Targeted STD detection and treatment should have a central role in HIV prevention in these emerging epidemics.

Joint modelling of multivariate longitudinal profiles: pitfalls of the random-effects approach

Due to its flexibility, the random-effects approach for the joint modelling of multivariate longitudinal profiles received a lot of attention in recent publications. In this approach different mixed models are joined by specifying a common distribution for their random-effects. Parameter estimates of this common distribution can then be used to evaluate the relation between the different responses. Using bivariate longitudinal measurements on pure-tone hearing thresholds, it will be shown that such a random-effects approach can yield misleading results for evaluating this relationship.

Can antiretroviral therapy be used to prevent sexual transmission of human immunodeficiency virus type 1?

Approximately 5 million people annually are newly infected with human immunodeficiency virus (HIV). Although education, behavior modification, and promotion of condom use are effective transmission-prevention measures, the severity of the pandemic demands that all possible prevention strategies be explored. Antiretroviral therapy has the potential to decrease sexual transmission of HIV type 1 by reducing levels of HIV RNA and thus decreasing the risk that infected persons will transmit the disease or by its use as preexposure or postexposure prophylaxis. In this article, we explore the rationale for using antiretroviral therapy to prevent sexual transmission of HIV, as well as the limitations of this approach.