Mindfulness-Based Stress Reduction for Symptom Management in Older Individuals with HIV-Associated Neurocognitive Disorder

The growing number of people aging with HIV represents a group vulnerable to the symptom burdens of HIV-associated neurocognitive disorder (HAND). Among younger groups, Mindfulness-Based Stress Reduction (MBSR) has been shown to help people living with HIV manage HIV-related and other life stress, and although there is some theoretical and empirical evidence that it may be effective among those with cognitive deficits, the approach has not been studied in older populations with HAND. Participants (n = 180) 55 years or older with HIV and cognitive impairment were randomly assigned to either an 8-week MBSR arm or a waitlist control. We assessed the impact of MBSR compared to a waitlist control on psychological outcomes [stress, anxiety, depression, and quality of life (QOL)] and cognitive metrics (e.g., speed of information processing, working memory, attention, impulsivity) measured at baseline, immediately post intervention (8 weeks) and one month later (16 weeks). Intent to treat analyses showed significant improvement in the MBSR group compared to control on symptoms of depression from baseline to 8 weeks, however, the difference was not sustained at 16 weeks. The MBSR group also showed improvement in perceived QOL from baseline to 16 weeks compared to the waitlist control group. Cognitive performance did not differ between the two treatment arms. MBSR shows promise as a tool to help alleviate the symptom burden of depression and low QOL in older individuals living with HAND and future work should address methods to better sustain the beneficial impact on depression and QOL.

Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models

During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of routine asymptomatic testing among UCSD students from 11/2020 to 11/2021, we develop hierarchical classification/decision tree models to select the most informative wastewater features (patterns of results) which predict individual infections. We find that the best predictor of positive individual level tests in residence buildings is whether or not the wastewater samples were positive in at least 3 of the past 7 days. We also demonstrate that the tree models outperform a wide range of other statistical and machine models in predicting the individual COVID-19 infections while preserving interpretability. Results of this study have been used to refine campus-wide guidelines and email notification systems to alert residents of potential infections.

Impact of antiretroviral therapy intensification with C-C motif chemokine receptor 5 antagonist maraviroc on HIV-associated neurocognitive impairment

OBJECTIVES

Chemokine receptor CCR5 is the principal co-receptor for entry of M-tropic HIV virus into immune cells. It is expressed in the central nervous system and may contribute to neuro-inflammation. The CCR5 antagonist maraviroc (MVC) has been suggested to improve HIV-associated neurocognitive impairment (NCI).

DESIGN

A double-blind, placebo-controlled, 48-week, randomized study of MVC vs. placebo in people with HIV (PWH) on stable antiretroviral therapy (ART) for more than one year in Hawaii and Puerto Rico with plasma HIV RNA less than 50 copies/ml and at least mild NCI defined as an overall or domain-specific neuropsychological z (NPZ) score less than -0.5.

METHODS

Study participants were randomized 2 : 1 to intensification of ART with MVC vs. placebo. The primary endpoint was change in global and domain-specific NPZ modeled from study entry to week 48. Covariate adjusted treatment comparisons of average changes in cognitive outcome were performed using winsorized NPZ data. Monocyte subset frequencies and chemokine expression as well as plasma biomarker levels were assessed.

RESULTS

Forty-nine participants were enrolled with 32 individuals randomized to MVC intensification and 17 to placebo. At baseline, worse NPZ scores were seen in the MVC arm. Comparison of 48-week NPZ change by arm revealed no differences except for a modest improvement in the Learning and Memory domain in the MVC arm, which did not survive multiplicity correction. No significant changes between arms were seen in immunologic parameters.

CONCLUSION

This randomized controlled study found no definitive evidence in favor of MVC intensification among PWH with mild cognitive difficulties.

Framework for converting mechanistic network models to probabilistic models

There are two prominent paradigms for the modelling of networks: in the first, referred to as the mechanistic approach, one specifies a set of domain-specific mechanistic rules that are used to grow or evolve the network over time; in the second, referred to as the probabilistic approach, one describes a model that specifies the likelihood of observing a given network. Mechanistic models (models developed based on the mechanistic approach) are appealing because they capture scientific processes that are believed to be responsible for network generation; however, they do not easily lend themselves to the use of inferential techniques when compared with probabilistic models. We introduce a general framework for converting a mechanistic network model (MNM) to a probabilistic network model (PNM). The proposed framework makes it possible to identify the essential network properties and their joint probability distribution for some MNMs; doing so makes it possible to address questions such as whether two different mechanistic models generate networks with identical distributions of properties, or whether a network property, such as clustering, is over- or under-represented in the networks generated by the model of interest compared with a reference model. The proposed framework is intended to bridge some of the gap that currently exists between the formulation and representation of mechanistic and PNMs. We also highlight limitations of PNMs that need to be addressed in order to close this gap.

Identification of system-level features in HIV migration within a host

OBJECTIVE

Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts.

METHOD

Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies of HIV migration among tissues as a network and model these networks using the family of exponential random graph models (ERGMs). ERGMs allow for the statistical assessment of whether network features occur more (or less) frequently in viral migration than might be expected by chance. The analysis investigates five potential features of the viral migration network: (1) bi-directional flow between tissues; (2) preferential migration among tissues in the same biological system; (3) heterogeneity in the level of viral migration related to HIV reservoir size; (4) hierarchical structure of migration; and (5) cyclical migration among several tissues. We calculate the Cohran's Q statistic to assess heterogeneity in the magnitude of the presence of these features across hosts. The analysis adjusts for missing data on body tissues.

RESULTS

We observe strong evidence for bi-directional flow between tissues; migration among tissues in the same biological system; and hierarchical structure of the viral migration network. This analysis shows no evidence for differential level of viral migration with respect to the HIV reservoir size of a tissue. There is evidence that cyclical migration among three tissues occurs less frequent than expected given the amount of viral migration. The analysis also provides evidence for heterogeneity in the magnitude that these features are present across hosts. Adjusting for missing tissue data identifies system-level features within a host as well as heterogeneity in the presence of these features across hosts that are not detected when the analysis only considers the observed data.

DISCUSSION

Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes.

Estimating contact network properties by integrating multiple data sources associated with infectious diseases

To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.

A platform trial design for preventive vaccines against Marburg virus and other emerging infectious disease threats

BACKGROUND

The threat of a possible Marburg virus disease outbreak in Central and Western Africa is growing. While no Marburg virus vaccines are currently available for use, several candidates are in the pipeline. Building on knowledge and experiences in the designs of vaccine efficacy trials against other pathogens, including SARS-CoV-2, we develop designs of randomized Phase 3 vaccine efficacy trials for Marburg virus vaccines.

METHODS

A core protocol approach will be used, allowing multiple vaccine candidates to be tested against controls. The primary objective of the trial will be to evaluate the effect of each vaccine on the rate of virologically confirmed Marburg virus disease, although Marburg infection assessed via seroconversion could be the primary objective in some cases. The overall trial design will be a mixture of individually and cluster-randomized designs, with individual randomization done whenever possible. Clusters will consist of either contacts and contacts of contacts of index cases, that is, ring vaccination, or other transmission units.

RESULTS

The primary efficacy endpoint will be analysed as a time-to-event outcome. A vaccine will be considered successful if its estimated efficacy is greater than 50% and has sufficient precision to rule out that true efficacy is less than 30%. This will require approximately 150 total endpoints, that is, cases of confirmed Marburg virus disease, per vaccine/comparator combination. Interim analyses will be conducted after 50 and after 100 events. Statistical analysis of the trial will be blended across the different types of designs. Under the assumption of a 6-month attack rate of 1% of the participants in the placebo arm for both the individually and cluster-randomized populations, the most likely sample size is about 20,000 participants per arm.

CONCLUSION

This event-driven design takes into the account the potentially sporadic spread of Marburg virus. The proposed trial design may be applicable for other pathogens against which effective vaccines are not yet available.

COVID-19 vaccination is protective of clinical disease in solid organ transplant recipients

BACKGROUND

Clinical effectiveness of coronavirus disease 2019 (COVID-19) vaccination in solid organ transplant recipients (SOTR) is not well documented despite multiple studies demonstrating sub-optimal immunogenicity.

METHODS

We reviewed medical records of eligible SOTRs at a single center to assess vaccination status and identify cases of symptomatic COVID-19 from 1/1/2021-8/12/2021. We developed a Cox proportional hazards model using date of vaccination and time since transplantation as a time varying covariate with age and gender as potential time-invariant confounders. Survival curves were created using the parameters estimated from the Cox model.

RESULTS

Among 1904 SOTRs, 1362 were fully vaccinated (96% received mRNA vaccines) and 542 were either unvaccinated (n = 470) or partially vaccinated (n = 72). There were 115 cases of COVID-19, of which 12 occurred in fully vaccinated individuals. Cox regression with date of vaccination and time since transplantation as the time-varying co-variates showed that after baseline adjustment for age and sex, being fully vaccinated had a significantly lower hazard for COVID-19, hazard ratio = 0.29, 95% confidence interval (0.09, 0.91).

CONCLUSION

We found that 2-dose mRNA COVID-19 vaccination was protective of symptomatic COVID-19 in vaccinated vs. unvaccinated SOTRs.

TWEET

COVID-19 vaccination was associated with significantly lower hazard for symptomatic COVID-19 (HR 0.29, 95% CI 0.09, 0.91) among 1904 SOT recipients at a single center from 1/1/2021-8/12/2021. This article is protected by copyright. All rights reserved.

Investigation of patient-sharing networks using a Bayesian network model selection approach for congruence class models

A Bayesian approach to conduct network model selection is presented for a general class of network models referred to as the congruence class models (CCMs). CCMs form a broad class that includes as special cases several common network models, such as the Erdős-Rényi-Gilbert model, stochastic block model, and many exponential random graph models. Due to the range of models that can be specified as CCMs, our proposed method is better able to select models consistent with generative mechanisms associated with observed networks than are current approaches. In addition, our approach allows for incorporation of prior information. We illustrate the use of this approach to select among several different proposed mechanisms for the structure of patient-sharing networks; such networks have been found to be associated with the cost and quality of medical care. We found evidence in support of heterogeneity in sociality but not selective mixing by provider type or degree.

Accounting for confounding by time, early intervention adoption, and time-varying effect modification in the design and analysis of stepped-wedge designs: application to a proposed study design to reduce opioid-related mortality

BACKGROUND

Beginning in 2019, stepped-wedge designs (SWDs) were being used in the investigation of interventions to reduce opioid-related deaths in communities across the United States. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters.

METHODS

We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of intervention components, and time-varying effect modification- in which external factors differentially impact intervention and control clusters.

RESULTS

In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error.

CONCLUSIONS

Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to how these external factors impact study endpoints and what estimands are most appropriate in the presence of such factors.

Evaluation of SARS-CoV-2 transmission mitigation strategies on a university campus using an agent-based network model

Universities are faced with decisions on how to resume campus activities while mitigating SARS-CoV-2 risk. To provide guidance for these decisions, we developed an agent-based network model of SARS-CoV-2 transmission to assess the potential impact of strategies to reduce outbreaks. The model incorporates important features related to risk at the University of California San Diego. We found that structural interventions for housing (singles only) and instructional changes (from in-person to hybrid with class size caps) can substantially reduce R0, but masking and social distancing are required to reduce this to at or below 1. Within a risk mitigation scenario, increased frequency of asymptomatic testing from monthly to twice weekly has minimal impact on average outbreak size (1.1-1.9), but substantially reduces the maximum outbreak size and cumulative number of cases. We conclude that an interdependent approach incorporating risk mitigation, viral detection, and public health intervention is required to mitigate risk.

Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention

OBJECTIVES

The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems.

METHODS

Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem.

RESULTS

We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated.

CONCLUSIONS

The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.

The Relationship Between the Human Immunodeficiency Virus-1 Transmission Network and the HIV Care Continuum in Los Angeles County

BACKGROUND

Public health action combating human immunodeficiency virus (HIV) includes facilitating navigation through the HIV continuum of care: timely diagnosis followed by linkage to care and initiation of antiretroviral therapy to suppress viral replication. Molecular epidemiology can identify rapidly growing HIV genetic transmission clusters. How progression through the care continuum relates to transmission clusters has not been previously characterized.

METHODS

We performed a retrospective study on HIV surveillance data from 5226 adult cases in Los Angeles County diagnosed from 2010 through 2014. Genetic transmission clusters were constructed using HIV-TRACE. Cox proportional hazard models were used to estimate the impact of transmission cluster growth on the time intervals between care continuum events. Gamma frailty models incorporated the effect of heterogeneity associated with genetic transmission clusters.

RESULTS

In contrast to our expectations, there were no differences in time to the care continuum events among individuals in clusters with different growth dynamics. However, upon achieving viral suppression, individuals in high growth clusters were slower to experience viral rebound (hazard ratio 0.83, P = .011) compared with individuals in low growth clusters. Heterogeneity associated with cluster membership in the timing to each event in the care continuum was highly significant (P < .001), with and without adjustment for transmission risk and demographics.

CONCLUSIONS

Individuals within the same transmission cluster have more similar trajectories through the HIV care continuum than those across transmission clusters. These findings suggest molecular epidemiology can assist public health officials in identifying clusters of individuals who may benefit from assistance in navigating the HIV care continuum.

Dynamic Network Prediction

We present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the US Senate from 2003-2016.

Accounting for Confounding by Time, Early Intervention Adoption, and Time-Varying Effect Modification in the Design and Analysis of Stepped-Wedge Designs: Application to a Proposed Study Design to Reduce Opioid-Related Mortality

Background: Stepped-wedge designs (SWDs) are currently being used in the investigation of interventions to reduce opioid-related deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and COVID-19 social distancing mandates. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods: We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of components of the intervention, and time-varying effect modificationâ€" in which external factors differentially impact intervention and control clusters. Results: In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions: Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects; misspecification can result in bias of the intervention effect estimate, under coverage of confidence intervals, and Type 1 error inflation. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to choosing appropriate models that account for potential external factors.

Mindfulness-based stress reduction for HIV-associated neurocognitive disorder: Rationale and protocol for a randomized controlled trial in older adults

The symptom burden of HIV-associated neurocognitive disorder (HAND) is high among older individuals, and treatment options are limited. Mindfulness-based stress reduction (MBSR) has potential to improve neurocognitive performance, psychosocial wellbeing, and quality of life, but empirical studies in this growing vulnerable population are lacking. In this trial, participants (N = 180) age 55 and older who are living with HIV infection, are on combination antiretroviral therapy with suppressed viral loads, and yet continue to experience behavioral and cognitive symptoms of HAND, are randomized to MBSR or to a waitlist control arm that receives MBSR following a 16-week period of standard care. Primary outcomes (attention, executive function, stress, anxiety, depression, everyday functioning, quality of life) and potential mediators (affect, mindfulness) and moderators (social support, loneliness) are assessed at baseline and weeks 8, 16, and 48 in both groups, with an additional assessment at week 24 (post-MBSR) in the crossover control group. Assessments include self-report and objective measures (e.g., neuropsychological assessment, neurological exam, clinical labs). In addition, a subset of participants (n = 30 per group) are randomly selected to undergo fMRI to evaluate changes in functional connectivity networks and their relationship to changes in neuropsychological outcomes. Forthcoming findings from this randomized controlled trial have the potential to contribute to a growing public health need as the number of older adults with HAND is expected to rise.

Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption

OBJECTIVE

To compare empirical and mechanistic modeling approaches for describing HIV-1 RNA viral load trajectories after antiretroviral treatment interruption and for identifying factors that predict features of viral rebound process.

METHODS

We apply and compare two modeling approaches in analysis of data from 346 participants in six AIDS Clinical Trial Group studies. From each separate analysis, we identify predictors for viral set points and delay in rebound. Our empirical model postulates a parametric functional form whose parameters represent different features of the viral rebound process, such as rate of rise and viral load set point. The viral dynamics model augments standard HIV dynamics models-a class of mathematical models based on differential equations describing biological mechanisms-by including reactivation of latently infected cells and adaptive immune response. We use Monolix, which makes use of a Stochastic Approximation of the Expectation-Maximization algorithm, to fit non-linear mixed effects models incorporating observations that were below the assay limit of quantification.

RESULTS

Among the 346 participants, the median age at treatment interruption was 42. Ninety-three percent of participants were male and sixty-five percent, white non-Hispanic. Both models provided a reasonable fit to the data and can accommodate atypical viral load trajectories. The median set points obtained from two approaches were similar: 4.44 log10 copies/mL from the empirical model and 4.59 log10 copies/mL from the viral dynamics model. Both models revealed that higher nadir CD4 cell counts and ART initiation during acute/recent phase were associated with lower viral set points and identified receiving a non-nucleoside reverse transcriptase inhibitor (NNRTI)-based pre-ATI regimen as a predictor for a delay in rebound.

CONCLUSION

Although based on different sets of assumptions, both models lead to similar conclusions regarding features of viral rebound process.

Accounting for external factors and early intervention adoption in the design and analysis of stepped-wedge designs: Application to a proposed study design to reduce opioid-related mortality

BACKGROUND

Stepped-wedge designs (SWDs) are currently being used to investigate interventions to reduce opioid overdose deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and social distancing orders due to the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the proposed intervention as they become widely available. These types of events induce confounding of the intervention effect by time. Such confounding is a well-known limitation of SWDs; a common approach to adjusting for it makes use of a mixed effects modeling framework that includes both fixed and random effects for time. However, these models have several shortcomings when multiple confounding factors are present.

METHODS

We discuss the limitations of existing methods based on mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce mortality associated with the opioid epidemic, and propose solutions to accommodate deviations from assumptions that underlie these models. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models under different sources of confounding. We specifically examine the impact of factors external to the study and premature adoption of intervention components.

RESULTS

When only external factors are present, our simulation studies show that commonly used mixed effects models can result in unbiased estimates of the intervention effect, but have inflated Type 1 error and result in under coverage of confidence intervals. These models are severely biased when confounding factors differentially impact intervention and control clusters; premature adoption of intervention components is an example of this scenario. In these scenarios, models that incorporate fixed intervention-by-time interaction terms and an unstructured covariance for the intervention-by-cluster-by-time random effects result in unbiased estimates of the intervention effect, reach nominal confidence interval coverage, and preserve Type 1 error, but may reduce power.

CONCLUSIONS

The incorporation of fixed and random time effects in mixed effects models require certain assumptions about the impact of confounding by time in SWD. Violations of these assumptions can result in severe bias of the intervention effect estimate, under coverage of confidence intervals, and inflated Type 1 error. Since model choice has considerable impact on study power as well as validity of results, careful consideration needs to be given to choosing an appropriate model that takes into account potential confounding factors.

Design of vaccine efficacy trials during public health emergencies

Public health emergencies, such as an Ebola disease outbreak, provide a complex and challenging environment for the evaluation of candidate vaccines. Here, we outline the need for flexible and responsive vaccine trial designs to be used in public health emergencies, and we summarize recommendations for their use in this setting.

Modelling testing frequencies required for early detection of a SARS-CoV-2 outbreak on a university campus

BACKGROUND

Early detection and risk mitigation efforts are essential for averting large outbreaks of SARS-CoV-2. Active surveillance for SARS-CoV-2 can aid in early detection of outbreaks, but the testing frequency required to identify an outbreak at its earliest stage is unknown. We assess what testing frequency is required to detect an outbreak before there are 10 detectable infections.

METHODS

A dynamic compartmental transmission model of SARS-CoV-2 was developed to simulate spread among a university community. After introducing a single infection into a fully susceptible population, we calculate the probability of detecting at least one case on each succeeding day with various NAT testing frequencies (daily testing achieving 25%, 50%, 75%, and 100% of the population tested per month) assuming an 85% test sensitivity. A proportion of infected individuals (varied from 1-60%) are assumed to present to health services (HS) for symptomatic testing. We ascertain the expected number of detectable infections in the community when there is a >90% probability of detecting at least 1 case. Sensitivity analyses examine impact of transmission rates (Rt=0=2, 2.5,3), presentation to HS (1%/5%/30%/60%), and pre-existing immunity (0%/10%) Results: Assuming an 85% test sensitivity, identifying an outbreak with 90% probability when the expected number of detectable infections is 9 or fewer requires NAT testing of 100% of the population per month; this result holds for all transmission rates and all levels of presentation at health services we considered. . If 1% of infected people present at HS and Rt=0=3, testing 75%/50%/25% per month could identify an outbreak when the expected numbers of detectable infections are 12/17/30 respectively; these numbers decline to 9/11/12 if 30% of infected people present at HS . As proportion of infected individuals present at health services increases, the marginal impact of active surveillance is reduced. Higher transmission rates result in shorter time to detection but also rapidly escalating cases without intervention. Little differences were observed with 10% pre-existing immunity.

CONCLUSIONS

Widespread testing of 100% of the campus population every month is required to detect an outbreak when there are fewer than 9 detectable infections for the scenarios examined, but high presentation of symptomatic people at HS can compensate in part for lower levels of testing. Early detection is necessary, but not sufficient, to curtail disease outbreaks; the proposed testing rates would need to be accompanied by case isolation, contact tracing, quarantine, and other risk mitigation and social distancing interventions.

Novel methods for the analysis of stepped wedge cluster randomized trials

Stepped wedge cluster randomized trials (SW-CRTs) have become increasingly popular and are used for a variety of interventions and outcomes, often chosen for their feasibility advantages. SW-CRTs must account for time trends in the outcome because of the staggered rollout of the intervention. Robust inference procedures and nonparametric analysis methods have recently been proposed to handle such trends without requiring strong parametric modeling assumptions, but these are less powerful than model-based approaches. We propose several novel analysis methods that reduce reliance on modeling assumptions while preserving some of the increased power provided by the use of mixed effects models. In one method, we use the synthetic control approach to find the best matching clusters for a given intervention cluster. Another method makes use of within-cluster crossover information to construct an overall estimator. We also consider methods that combine these approaches to further improve power. We test these methods on simulated SW-CRTs, describing scenarios in which these methods have increased power compared with existing nonparametric methods while preserving nominal validity when mixed effects models are misspecified. We also demonstrate theoretical properties of these estimators with less restrictive assumptions than mixed effects models. Finally, we propose avenues for future research on the use of these methods; motivation for such research arises from their flexibility, which allows the identification of specific causal contrasts of interest, their robustness, and the potential for incorporating covariates to further increase power. Investigators conducting SW-CRTs might well consider such methods when common modeling assumptions may not hold.

Noise-induced Cochlear Synaptopathy with and Without Sensory Cell Loss

Prior work has provided extensive documentation of threshold sensitivity and sensory hair cell losses after noise exposure. It is now clear, however, that cochlear synaptic loss precedes such losses, at least at low-moderate noise doses, silencing affected neurons. To address questions of whether, and how, cochlear synaptopathy and underlying mechanisms change as noise dose is varied, we assessed cochlear physiologic and histologic consequences of a range of exposures varied in duration from 15 min to 8 h and in level from 85 to 112 dB SPL. Exposures delivered to adult CBA/CaJ mice produced acute elevations in hair cell- and neural-based response thresholds ranging from trivial (∼5 dB) to large (∼50 dB), followed by varying degrees of recovery. Males appeared more noise vulnerable for some conditions of exposure. There was little to no inner hair cell (IHC) loss, but outer hair cell (OHC) loss could be substantial at highest frequencies for highest noise doses. Synapse loss was an early manifestation of noise injury and did not scale directly with either temporary or permanent threshold shift. With increasing noise dose, synapse loss grew to ∼50%, then declined for exposures yielding permanent hair cell injury/loss. All synaptopathic, but no non-synaptopathic exposures produced persistent neural response amplitude declines; those additionally yielding permanent OHC injury/loss also produced persistent reductions in OHC-based responses and exaggerated neural amplitude declines. Findings show that widespread cochlear synaptopathy can be present with and without noise-induced sensory cell loss and that differing patterns of cellular injury influence synaptopathic outcomes.

Antiretroviral Therapy and Mortality in Rural South Africa: A Comparison of Causal Modeling Approaches

Estimation of causal effects from observational data is a primary goal of epidemiology. The use of multiple methods with different assumptions relating to exchangeability improves causal inference by demonstrating robustness across assumptions. We estimated the effect of antiretroviral therapy (ART) on mortality in rural KwaZulu-Natal, South Africa, from 2007 to 2011, using 2 methods with substantially different assumptions: the regression discontinuity design (RDD) and inverse-probability–weighted (IPW) marginal structural models (MSMs). The RDD analysis took advantage of a CD4-cell-count–based threshold for ART initiation (200 cells/μL). The 2 methods yielded consistent but nonidentical results for the effect of immediate initiation of ART (RDD intention-to-treat hazard ratio (HR) = 0.66, 95% confidence interval (CI): 0.35, 1.26; RDD complier average causal effect HR = 0.56, 95% CI: 0.41, 0.77; IPW MSM HR = 0.49, 95% CI: 0.42, 0.58). Although RDD and IPW MSM estimates have distinct identifying assumptions, strengths, and limitations in terms of internal and external validity, results in this application were similar. The differences in modeling approaches and the external validity of each method may explain the minor differences in effect estimates. The overall consistency of the results lends support for causal inference about the effect of ART on mortality from these data.

A Sexual Partnership Duration: Characterizing Sampling Conditions That Permit unbiased Estimation of Survivorship and Effect on It of Covariates

Partnership duration data are commonly obtained through surveys that collect information on relationships that are ongoing during a fixed time window. This sampling mechanism leads to duration data that are left truncated and right censored; such data have been analysed using the standard truncation product limit estimator (TPLE). In this paper, we describe a common sampling scheme for collecting sexual partnership data, discuss a key assumption required for the TPLE to be unbiased, and provide the conditions under which the nonparametric maximum likelihood estimator of the relationship duration distribution is unique and consistent. We also investigate the conditions required for the consistency of the regression coefcient from a Cox proportional hazards model that apply even when the distribution of duration is not completely identifiable due to restrictions on the support of the truncation distribution. Lastly, we will provide some illustrative examples on estimating distribution of most recent partnerships and present spline regression results based on partnership data collected from sexual behavior survey in Mochudi, Botswana.

Impact of early antiretroviral therapy eligibility on HIV acquisition: household-level evidence from rural South Africa

Objectives:

We investigate the effect of immediate antiretroviral therapy (ART) eligibility on HIV incidence among HIV-uninfected household members.

Design:

Regression discontinuity study arising from a population-based cohort.

Methods:

Household members of patients seeking care at the Hlabisa HIV Treatment and Care Programme in rural KwaZulu-Natal South Africa between January 2007 and August 2011 with CD4⁺ cell counts up to 350 cells/μl were eligible for inclusion if they had at least two HIV tests and were HIV-uninfected at the time the index patient linked to care (N = 4115). Regression discontinuity was used to assess the intention-to-treat effect of immediate versus delayed ART eligibility on HIV incidence among household members. Exploiting the CD4⁺ cell count-based threshold rule for ART initiation (CD4⁺ < 200 cells/μl until August 2011), we used Cox proportional hazards models to compare outcomes for household members of patients who presented for care with CD4⁺ cell counts just above versus just below the ART initiation threshold.

Results:

Characteristics of household members of index patients initiating HIV care were balanced between those with an index patient immediately eligible for ART (N = 2489) versus delayed for ART (N = 1626). There were 337 incident HIV infections among household members, corresponding to an HIV incidence of 2.4 infections per 100 person-years (95% confidence interval 2.5–3.1). Immediate eligibility for treatment reduced HIV incidence in households by 47% in our optimal estimate (hazard ratio = 0.53, 95% confidence interval 0.30–0.96), and by 32–60% in alternate specifications of the model.

Conclusion:

Immediate eligibility of ART led to substantial reductions in household-level HIV incidence.

High HIV-1 RNA Among Newly Diagnosed People in Botswana

HIV-1 RNA level is strongly associated with HIV transmission risk. We sought to determine whether HIV-1 RNA level was associated with prior knowledge of HIV status among treatment-naive HIV-infected individuals in Botswana, a country with high rates of antiretroviral treatment (ART) coverage. This information may be helpful in targeting HIV diagnosis and treatment efforts in similar high HIV prevalence settings in a population-based survey. HIV-infected individuals were identified during a household survey performed in 30 communities across Botswana. ART-naive persons with detectable HIV-1 RNA (>400 copies/mL) were divided into two groups, newly diagnosed and individuals tested in the past who knew about their HIV infection at the time of household visit, but had not taken ART. Levels of HIV-1 RNA were compared between groups, overall and by age and gender. Among 815 HIV-infected ART-naive persons with detectable virus, newly diagnosed individuals had higher levels of HIV-1 RNA (n = 490, median HIV-1 RNA 4.35, interquartile range (IQR) 3.79-4.91 log₁₀ copies/mL) than those who knew about their HIV-positive status (n = 325, median HIV-1 RNA 4.10, IQR 3.55-4.68 log₁₀ copies/mL; p values <.001, but p value = .011 after adjusting for age and gender). A nonsignificant trend for higher HIV-1 RNA was found among newly diagnosed men 30 years of age or older (median HIV-1 RNA 4.58, IQR 4.07-5.02 log₁₀ copies/mL vs. 4.17, 3.61-4.71 log₁₀ copies/mL). Newly diagnosed individuals have elevated levels of HIV-1 RNA. This study highlights the need for early diagnosis and treatment of HIV infection for purposes of HIV epidemic control, even in a setting with high ART coverage.

The use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials

We investigate the use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time-to-event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster-randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair-specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply the permutation tests to a cluster-randomized trial evaluating the effect of an intervention to reduce the incidence of hospital-acquired infection. In some settings, outcomes from different clusters may be correlated, and we evaluate the validity and efficiency of permutation test in such settings. Lastly, we propose a permutation test for stepped-wedge designs and compare its performance with mixed-effect modeling and illustrate its superiority when sample sizes are small, the underlying distribution is skewed, or there is correlation across clusters. Copyright © 2017 John Wiley & Sons, Ltd.

Inference on network statistics by restricting to the network space: applications to sexual history data

Analysis of sexual history data intended to describe sexual networks presents many challenges arising from the fact that most surveys collect information on only a very small fraction of the population of interest. In addition, partners are rarely identified and responses are subject to reporting biases. Typically, each network statistic of interest, such as mean number of sexual partners for men or women, is estimated independently of other network statistics. There is, however, a complex relationship among networks statistics; and knowledge of these relationships can aid in addressing concerns mentioned earlier. We develop a novel method that constrains a posterior predictive distribution of a collection of network statistics in order to leverage the relationships among network statistics in making inference about network properties of interest. The method ensures that inference on network properties is compatible with an actual network. Through extensive simulation studies, we also demonstrate that use of this method can improve estimates in settings where there is uncertainty that arises both from sampling and from systematic reporting bias compared with currently available approaches to estimation. To illustrate the method, we apply it to estimate network statistics using data from the Chicago Health and Social Life Survey. Copyright © 2017 John Wiley & Sons, Ltd.

Leveraging contact network structure in the design of cluster randomized trials

BACKGROUND

In settings like the Ebola epidemic, where proof-of-principle trials have provided evidence of efficacy but questions remain about the effectiveness of different possible modes of implementation, it may be useful to conduct trials that not only generate information about intervention effects but also themselves provide public health benefit. Cluster randomized trials are of particular value for infectious disease prevention research by virtue of their ability to capture both direct and indirect effects of intervention, the latter of which depends heavily on the nature of contact networks within and across clusters. By leveraging information about these networks-in particular the degree of connection across randomized units, which can be obtained at study baseline-we propose a novel class of connectivity-informed cluster trial designs that aim both to improve public health impact (speed of epidemic control) and to preserve the ability to detect intervention effects.

METHODS

We several designs for cluster randomized trials with staggered enrollment, in each of which the order of enrollment is based on the total number of ties (contacts) from individuals within a cluster to individuals in other clusters. Our designs can accommodate connectivity based either on the total number of external connections at baseline or on connections only to areas yet to receive the intervention. We further consider a "holdback" version of the designs in which control clusters are held back from re-randomization for some time interval. We investigate the performance of these designs in terms of epidemic control outcomes (time to end of epidemic and cumulative incidence) and power to detect intervention effect, by simulating vaccination trials during an SEIR-type epidemic outbreak using a network-structured agent-based model. We compare results to those of a traditional Stepped Wedge trial.

RESULTS

In our simulation studies, connectivity-informed designs lead to a 20% reduction in cumulative incidence compared to comparable traditional study designs, but have little impact on epidemic length. Power to detect intervention effect is reduced in all connectivity-informed designs, but "holdback" versions provide power that is very close to that of a traditional Stepped Wedge approach.

CONCLUSION

Incorporating information about cluster connectivity in the design of cluster randomized trials can increase their public health impact, especially in acute outbreak settings. Using this information helps control outbreaks-by minimizing the number of cross-cluster infections-with very modest cost in terms of power to detect effectiveness.

Antiretroviral Therapy to Prevent HIV Acquisition in Serodiscordant Couples in a Hyperendemic Community in Rural South Africa

We assessed the role of antiretroviral therapy (ART) on human immunodeficiency virus (HIV) acquisition in serodiscordant couples in KwaZulu-Natal, South Africa. ART use was associated with a 77% reduction in HIV acquisition risk, suggesting ART is highly effective for prevention in population-based settings.

Background. Antiretroviral therapy (ART) was highly efficacious in preventing human immunodeficiency virus (HIV) transmission in stable serodiscordant couples in the HPTN-052 study, a resource-intensive randomized controlled trial with near-perfect ART adherence and mutual HIV status disclosure among all participating couples. However, minimal evidence exists of the effectiveness of ART in preventing HIV acquisition in stable serodiscordant couples in “real-life” population-based settings in hyperendemic communities of sub-Saharan Africa, where health systems are typically resource-poor and overburdened, adherence to ART is often low, and partners commonly do not disclose their HIV status to each other.

Methods. Data arose from a population-based open cohort in KwaZulu-Natal, South Africa. A total of 17 016 HIV-uninfected individuals present between January 2005 and December 2013 were included. Interval-censored time-updated proportional hazards regression was used to assess how the ART status affected HIV transmission risk in stable serodiscordant relationships.

Results. We observed 1619 HIV seroconversions in 17 016 individuals, over 60 349 person-years follow-up time. During the follow-up period, 1846 individuals had an HIV-uninfected and 196 had an HIV-infected stable partner HIV incidence was 3.8/100 person-years (PY) among individuals with an HIV-infected partner (95% confidence interval [CI], 2.3–5.6), 1.4/100 PY (.4–3.5) among those with HIV-infected partners receiving ART, and 5.6/100 PY (3.5–8.4) among those with HIV-infected partners not receiving ART. Use of ART was associated with a 77% decrease in HIV acquisition risk among serodiscordant couples (adjusted hazard ratio, 0.23; 95% CI, .07–.80).

Conclusions. ART initiation was associated with a very large reduction in HIV acquisition in serodiscordant couples in rural KwaZulu-Natal. However, this “real-life” effect was substantially lower than the effect observed in the HPTN-052 trial. To eliminate HIV transmission in serodiscordant couples, additional prevention interventions are probably needed.

Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

An issue that remains challenging in the field of causal inference is how to relax the assumption of no interference between units. Interference occurs when the treatment of one unit can affect the outcome of another, a situation which is likely to arise with outcomes that may depend on social interactions, such as occurrence of infectious disease. Existing methods to accommodate interference largely depend upon an assumption of “partial interference” – interference only within identifiable groups but not among them. There remains a considerable need for development of methods that allow further relaxation of the no-interference assumption. This paper focuses on an estimand that is the difference in the outcome that one would observe if the treatment were provided to all clusters compared to that outcome if treatment were provided to none – referred as the overall treatment effect. In trials of infectious disease prevention, the randomized treatment effect estimate will be attenuated relative to this overall treatment effect if a fraction of the exposures in the treatment clusters come from individuals who are outside these clusters. This source of interference – contacts sufficient for transmission that are with treated clusters – is potentially measurable. In this manuscript, we leverage epidemic models to infer the way in which a given level of interference affects the incidence of infection in clusters. This leads naturally to an estimator of the overall treatment effect that is easily implemented using existing software.

Viral Genetic Linkage Analysis in the Presence of Missing Data

Analyses of viral genetic linkage can provide insight into HIV transmission dynamics and the impact of prevention interventions. For example, such analyses have the potential to determine whether recently-infected individuals have acquired viruses circulating within or outside a given community. In addition, they have the potential to identify characteristics of chronically infected individuals that make their viruses likely to cluster with others circulating within a community. Such clustering can be related to the potential of such individuals to contribute to the spread of the virus, either directly through transmission to their partners or indirectly through further spread of HIV from those partners. Assessment of the extent to which individual (incident or prevalent) viruses are clustered within a community will be biased if only a subset of subjects are observed, especially if that subset is not representative of the entire HIV infected population. To address this concern, we develop a multiple imputation framework in which missing sequences are imputed based on a model for the diversification of viral genomes. The imputation method decreases the bias in clustering that arises from informative missingness. Data from a household survey conducted in a village in Botswana are used to illustrate these methods. We demonstrate that the multiple imputation approach reduces bias in the overall proportion of clustering due to the presence of missing observations.

Sampling dynamic networks with application to investigation of HIV epidemic drivers

We propose a method for randomly sampling dynamic networks that permits isolation of the impact of different network features on processes that propagate on networks. The new methods permit uniform sampling of dynamic networks in ways that ensure that they are consistent with both a given cumulative network and with specified values for constraints on the dynamic network properties. Development of such methods is challenging because modifying one network property will generally tend to modify others as well. Methods to sample constrained dynamic networks are particularly useful in the investigation of network-based interventions that target and modify specific dynamic network properties, especially in settings where the whole network is unobservable and therefore many network properties are unmeasurable. We illustrate this method by investigating the incremental impact of changes in networks properties that are relevant for the spread of infectious diseases, such as concurrency in sexual relationships. Development of the method is motivated by the challenges that arise in investigating the role of HIV epidemic drivers due to the often limited information available about contact networks. The proposed methods for randomly sampling dynamic networks facilitate investigation of the type of network data that can best contribute to an understanding of the HIV epidemic dynamics as well as of the limitations of conclusions drawn in the absence of such information. Hence, the methods are intended to aid in the design and interpretation of studies of network-based interventions.

Sample size considerations in the design of cluster randomized trials of combination HIV prevention

Background Cluster randomized trials have been utilized to evaluate the effectiveness of HIV prevention strategies on reducing incidence. Design of such studies must take into account possible correlation of outcomes within randomized units. Purpose To discuss power and sample size considerations for cluster randomized trials of combination HIV prevention, using an HIV prevention study in Botswana as an illustration. Methods We introduce a new agent-based model to simulate the community-level impact of a combination prevention strategy and investigate how correlation structure within a community affects the coefficient of variation - an essential parameter in designing a cluster randomized trial. Results We construct collections of sexual networks and then propagate HIV on them to simulate the disease epidemic. Increasing level of sexual mixing between intervention and standard-of-care (SOC) communities reduces the difference in cumulative incidence in the two sets of communities. Fifteen clusters per arm and 500 incidence cohort members per community provide 95% power to detect the projected difference in cumulative HIV incidence between SOC and intervention communities (3.93% and 2.34%) at the end of the third study year, using a coefficient of variation 0.25. Although available formulas for calculating sample size for cluster randomized trials can be derived by assuming an exchangeable correlation structure within clusters, we show that deviations from this assumption do not generally affect the validity of such formulas. Limitations We construct sexual networks based on data from Likoma Island, Malawi, and base disease progression on longitudinal estimates from an incidence cohort in Botswana and in Durban as well as a household survey in Mochudi, Botswana. Network data from Botswana and larger sample sizes to estimate rates of disease progression would be useful in assessing the robustness of our model results. Conclusion Epidemic modeling plays a critical role in planning and evaluating interventions for prevention. Simulation studies allow us to take into consideration available information on sexual network characteristics, such as mixing within and between communities as well as coverage levels for different prevention modalities in the combination prevention package.

Linkage of viral sequences among HIV-infected village residents in Botswana: estimation of linkage rates in the presence of missing data

Linkage analysis is useful in investigating disease transmission dynamics and the effect of interventions on them, but estimates of probabilities of linkage between infected people from observed data can be biased downward when missingness is informative. We investigate variation in the rates at which subjects' viral genotypes link across groups defined by viral load (low/high) and antiretroviral treatment (ART) status using blood samples from household surveys in the Northeast sector of Mochudi, Botswana. The probability of obtaining a sequence from a sample varies with viral load; samples with low viral load are harder to amplify. Pairwise genetic distances were estimated from aligned nucleotide sequences of HIV-1C env gp120. It is first shown that the probability that randomly selected sequences are linked can be estimated consistently from observed data. This is then used to develop estimates of the probability that a sequence from one group links to at least one sequence from another group under the assumption of independence across pairs. Furthermore, a resampling approach is developed that accounts for the presence of correlation across pairs, with diagnostics for assessing the reliability of the method. Sequences were obtained for 65% of subjects with high viral load (HVL, n = 117), 54% of subjects with low viral load but not on ART (LVL, n = 180), and 45% of subjects on ART (ART, n = 126). The probability of linkage between two individuals is highest if both have HVL, and lowest if one has LVL and the other has LVL or is on ART. Linkage across groups is high for HVL and lower for LVL and ART. Adjustment for missing data increases the group-wise linkage rates by 40–100%, and changes the relative rates between groups. Bias in inferences regarding HIV viral linkage that arise from differential ability to genotype samples can be reduced by appropriate methods for accommodating missing data.

The analysis of viral genomes has great potential for investigating transmission of disease, including the identification of risk factors and transmission clusters, and can thereby aid in targeting interventions. To make use of genetic data in this way, it is necessary to make inferences about population-level patterns of viral linkage. As with any rigorous statistical inference from sampled data to a population, it is important to consider the effect of the sampling strategy and the occurrence of missing data on the final inferences made. In this paper we highlight the effects of missing data on the resulting estimates of population level linkage rates and develop methods for adjusting for the presence of missing data. As an example, we consider comparing the rates of linkage of HIV sequences from subjects with high viral load, low viral load, or on antiretroviral treatment, and show that comparative inferences are compromised when adjustment is not made for missing sequences and bias in inferences can be reduced with proper adjustment.

Locally efficient estimation of marginal treatment effects when outcomes are correlated: is the prize worth the chase?

Semiparametric methods have been developed to increase efficiency of inferences in randomized trials by incorporating baseline covariates. Locally efficient estimators of marginal treatment effects, which achieve minimum variance under an assumed model, are available for settings in which outcomes are independent. The value of the pursuit of locally efficient estimators in other settings, such as when outcomes are multivariate, is often debated. We derive and evaluate semiparametric locally efficient estimators of marginal mean treatment effects when outcomes are correlated; such outcomes occur in randomized studies with clustered or repeated-measures responses. The resulting estimating equations modify existing generalized estimating equations (GEE) by identifying the efficient score under a mean model for marginal effects when data contain baseline covariates. Locally efficient estimators are implemented for longitudinal data with continuous outcomes and clustered data with binary outcomes. Methods are illustrated through application to AIDS Clinical Trial Group Study 398, a longitudinal randomized clinical trial that compared the effects of various protease inhibitors in HIV-positive subjects who had experienced antiretroviral therapy failure. In addition, extensive simulation studies characterize settings in which locally efficient estimators result in efficiency gains over suboptimal estimators and assess their feasibility in practice.

FLEXIBLE COVARIATE-ADJUSTED EXACT TESTS OF RANDOMIZED TREATMENT EFFECTS WITH APPLICATION TO A TRIAL OF HIV EDUCATION

The primary goal of randomized trials is to compare the effects of different interventions on some outcome of interest. In addition to the treatment assignment and outcome, data on baseline covariates, such as demographic characteristics or biomarker measurements, are typically collected. Incorporating such auxiliary co-variates in the analysis of randomized trials can increase power, but questions remain about how to preserve type I error when incorporating such covariates in a flexible way, particularly when the number of randomized units is small. Using the Young Citizens study, a cluster randomized trial of an educational intervention to promote HIV awareness, we compare several methods to evaluate intervention effects when baseline covariates are incorporated adaptively. To ascertain the validity of the methods shown in small samples, extensive simulation studies were conducted. We demonstrate that randomization inference preserves type I error under model selection while tests based on asymptotic theory may yield invalid results. We also demonstrate that covariate adjustment generally increases power, except at extremely small sample sizes using liberal selection procedures. Although shown within the context of HIV prevention research, our conclusions have important implications for maximizing efficiency and robustness in randomized trials with small samples across disciplines.

A general implementation of TMLE for longitudinal data applied to causal inference in survival analysis

In many randomized controlled trials the outcome of interest is a time to event, and one measures on each subject baseline covariates and time-dependent covariates until the subject either drops-out, the time to event is observed, or the end of study is reached. The goal of such a study is to assess the causal effect of the treatment on the survival curve. We present a targeted maximum likelihood estimator of the causal effect of treatment on survival fully utilizing all the available covariate information, resulting in a double robust locally efficient substitution estimator that will be consistent and asymptotically linear if either the censoring mechanism is consistently estimated, or if the maximum likelihood based estimator is already consistent. In particular, under the independent censoring assumption assumed by current methods, this TMLE is always consistent and asymptotically linear so that it provides valid confidence intervals and tests. Furthermore, we show that when both the censoring mechanism and the initial maximum likelihood based estimator are mis-specified, and thus inconsistent, the TMLE exhibits stability when inverse probability weighted estimators and double robust estimating equation based methods break down The TMLE is used to analyze the Tshepo study, a study designed to evaluate the efficacy, tolerability, and development of drug resistance of six different first-line antiretroviral therapies. Most importantly this paper presents a general algorithm that may be used to create targeted maximum likelihood estimators of a large class of parameters of interest for general longitudinal data structures.

Efficacy and safety of three antiretroviral regimens for initial treatment of HIV-1: a randomized clinical trial in diverse multinational settings

BACKGROUND

Antiretroviral regimens with simplified dosing and better safety are needed to maximize the efficiency of antiretroviral delivery in resource-limited settings. We investigated the efficacy and safety of antiretroviral regimens with once-daily compared to twice-daily dosing in diverse areas of the world.

METHODS AND FINDINGS

1,571 HIV-1-infected persons (47% women) from nine countries in four continents were assigned with equal probability to open-label antiretroviral therapy with efavirenz plus lamivudine-zidovudine (EFV+3TC-ZDV), atazanavir plus didanosine-EC plus emtricitabine (ATV+DDI+FTC), or efavirenz plus emtricitabine-tenofovir-disoproxil fumarate (DF) (EFV+FTC-TDF). ATV+DDI+FTC and EFV+FTC-TDF were hypothesized to be non-inferior to EFV+3TC-ZDV if the upper one-sided 95% confidence bound for the hazard ratio (HR) was ≤1.35 when 30% of participants had treatment failure. An independent monitoring board recommended stopping study follow-up prior to accumulation of 472 treatment failures. Comparing EFV+FTC-TDF to EFV+3TC-ZDV, during a median 184 wk of follow-up there were 95 treatment failures (18%) among 526 participants versus 98 failures among 519 participants (19%; HR 0.95, 95% CI 0.72-1.27; p = 0.74). Safety endpoints occurred in 243 (46%) participants assigned to EFV+FTC-TDF versus 313 (60%) assigned to EFV+3TC-ZDV (HR 0.64, CI 0.54-0.76; p<0.001) and there was a significant interaction between sex and regimen safety (HR 0.50, CI 0.39-0.64 for women; HR 0.79, CI 0.62-1.00 for men; p = 0.01). Comparing ATV+DDI+FTC to EFV+3TC-ZDV, during a median follow-up of 81 wk there were 108 failures (21%) among 526 participants assigned to ATV+DDI+FTC and 76 (15%) among 519 participants assigned to EFV+3TC-ZDV (HR 1.51, CI 1.12-2.04; p = 0.007).

CONCLUSION

EFV+FTC-TDF had similar high efficacy compared to EFV+3TC-ZDV in this trial population, recruited in diverse multinational settings. Superior safety, especially in HIV-1-infected women, and once-daily dosing of EFV+FTC-TDF are advantageous for use of this regimen for initial treatment of HIV-1 infection in resource-limited countries. ATV+DDI+FTC had inferior efficacy and is not recommended as an initial antiretroviral regimen.

TRIAL REGISTRATION

www.ClinicalTrials.gov NCT00084136. Please see later in the article for the Editors' Summary.

Augmented generalized estimating equations for improving efficiency and validity of estimation in cluster randomized trials by leveraging cluster-level and individual-level covariates

Recent methodological advances in covariate adjustment in randomized clinical trials have used semiparametric theory to improve efficiency of inferences by incorporating baseline covariates; these methods have focused on independent outcomes. We modify one of these approaches, augmentation of standard estimators, for use within cluster randomized trials in which treatments are assigned to groups of individuals, thereby inducing correlation. We demonstrate the potential for imbalance correction and efficiency improvement through consideration of both cluster-level covariates and individual-level covariates. To improve small-sample estimation, we consider several variance adjustments. We evaluate this approach for continuous and binary outcomes through simulation and apply it to data from a cluster randomized trial of a community behavioral intervention related to HIV prevention in Tanzania.

Using HIV transmission networks to investigate community effects in HIV prevention trials

Effective population screening of HIV and prevention of HIV transmission are only part of the global fight against AIDS. Community-level effects, for example those aimed at thwarting future transmission, are potential outcomes of treatment and may be important in stemming the epidemic. However, current clinical trial designs are incapable of detecting a reduction in future transmission due to treatment. We took advantage of the fact that HIV is an evolving pathogen whose transmission network can be reconstructed using genetic sequence information to address this shortcoming. Here, we use an HIV transmission network inferred from recently infected men who have sex with men (MSM) in San Diego, California. We developed and tested a network-based statistic for measuring treatment effects using simulated clinical trials on our inferred transmission network. We explored the statistical power of this network-based statistic against conventional efficacy measures and find that when future transmission is reduced, the potential for increased statistical power can be realized. Furthermore, our simulations demonstrate that the network statistic is able to detect community-level effects (e.g., reduction in onward transmission) of HIV treatment in a clinical trial setting. This study demonstrates the potential utility of a network-based statistical metric when investigating HIV treatment options as a method to reduce onward transmission in a clinical trial setting.

A comparison of the efficacy of two interventions to reduce HIV risk behaviors among drug users

Numerous interventions have been developed and implemented to decrease risk behaviors which lead to HIV infection and transmission. These interventions have been differentially successful in reducing high risk behaviors in various populations. Testing and evaluation of the interventions have been subject to various degrees of rigor. The CDC recommends the use of interventions which have been rigorously tested and meet the standards for evidence based intervention rather than the continuation of the development of new interventions. Project RESPECT is an evidence based intervention that proved efficacious in increasing condom use among patients of STD clinics. We tested the efficacy of the RESPECT intervention against the NIDA standard intervention to determine if the RESPECT intervention was more effective in reducing high risk behaviors among drug users. Both interventions showed changes from baseline to follow-up; RESPECT was more effective than the NIDA standard intervention in reducing high risk sex behaviors.

Bayesian methods for fitting mixture models that characterize branching tree processes: An application to development of resistant TB strains

For pathogens that must be treated with combinations of antibiotics and acquire resistance through genetic mutation, knowledge of the order in which drug-resistance mutations occur may be important for determining treatment policies. Diagnostic specimens collected from patients are often available; this makes it possible to determine the presence of individual drug resistance-conferring mutations and combinations of these mutations. In most cases, these specimens are only available from a patient at a single point in time; it is very rare to have access to multiple specimens from a single patient collected over time as resistance accumulates to multiple drugs. Statistical methods that use branching trees have been successfully applied to such cross-sectional data to make inference on the ordering of events that occurred prior to sampling. Here, we propose a Bayesian approach to fitting branching tree models that has several advantages, including the ability to accommodate prior information regarding measurement error or cross resistance and the natural way it permits the characterization of uncertainty. Our methods are applied to a data set for drug-resistant TB in Peru; the goal of the analysis was to determine the order with which patients develop resistance to the drugs commonly used for treating TB in this setting.

Targeted maximum likelihood estimation of effect modification parameters in survival analysis

The Cox proportional hazards model or its discrete time analogue, the logistic failure time model, posit highly restrictive parametric models and attempt to estimate parameters which are specific to the model proposed. These methods are typically implemented when assessing effect modification in survival analyses despite their flaws. The targeted maximum likelihood estimation (TMLE) methodology is more robust than the methods typically implemented and allows practitioners to estimate parameters that directly answer the question of interest. TMLE will be used in this paper to estimate two newly proposed parameters of interest that quantify effect modification in the time to event setting. These methods are then applied to the Tshepo study to assess if either gender or baseline CD4 level modify the effect of two cART therapies of interest, efavirenz (EFV) and nevirapine (NVP), on the progression of HIV. The results show that women tend to have more favorable outcomes using EFV while males tend to have more favorable outcomes with NVP. Furthermore, EFV tends to be favorable compared to NVP for individuals at high CD4 levels.

Comparisons of anemia, thrombocytopenia, and neutropenia at initiation of HIV antiretroviral therapy in Africa, Asia, and the Americas

BACKGROUND

Hematological abnormalities are common manifestations of advanced HIV-1 infection that could affect the outcomes of highly-active antiretroviral therapy (HAART). Although most HIV-1-infected individuals live in resource-constrained countries, there is little information about the frequency of hematological abnormalities such as anemia, neutropenia, and thrombocytopenia among individuals with advanced HIV-1 disease.

METHODS

This study compared the prevalence of pre-antiretroviral therapy hematological abnormalities among 1571 participants in a randomized trial of antiretroviral efficacy in Africa, Asia, South America, the Caribbean, and the USA. Potential covariates for anemia, neutropenia, and thrombocytopenia were identified in univariate analyses and evaluated in separate multivariable models for each hematological condition.

RESULTS

The frequencies of neutropenia (absolute neutrophil count ≤1.3×10⁹/l), anemia (hemoglobin ≤10g/dl), and thrombocytopenia (platelets ≤125×10⁹/l) at initiation of antiretroviral therapy were 14%, 12%, and 7%, respectively, and varied by country (p<0.0001 for each). In multivariable models, anemia was associated with gender, platelet count, and country; neutropenia was associated with CD4+ lymphocyte and platelet counts; and thrombocytopenia was associated with country, gender, and chronic hepatitis B infection.

CONCLUSIONS

Differences in the frequency of pretreatment hematological abnormalities could have important implications for the choice of antiretroviral regimen in resource-constrained settings.