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Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences‐in‐differences rely instead on the parallel trends assumption, allowing for some types of time‐invariant unmeasured confounding. However, most existing difference‐in‐differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow‐up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g‐formula identifies the intervention‐specific mean under stable unit treatment value assumption, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse‐probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay‐at‐home order on all‐cause mortality during the COVID‐19 pandemic in spring 2020 in the United States. [ABSTRACT FROM AUTHOR]

In first-line antiretroviral therapy (ART) for human immunodeficiency virus (HIV) treatment, some subgroups of patients may respond better to an efavirenz-based regimen than an integrase strand transfer inhibitor (InSTI)-based regimen, or vice versa, due to patient characteristics modifying treatment effects. Using data based on nearly 16,000 patients from the North American AIDS Cohort Collaboration on Research and Design from 2009–2016, statistical methods for precision medicine were employed to estimate an optimal treatment rule that minimizes the 5-year risk of the composite outcome of acquired immune deficiency syndrome (AIDS)-defining illnesses, serious non-AIDS events, and all-cause mortality. The treatment rules considered were functions that recommend either an efavirenz- or InSTI-based regimen conditional on baseline patient characteristics such as demographic information, laboratory results, and health history. The estimated 5-year risk under the estimated optimal treatment rule was 10.0% (95% confidence interval (CI): 8.6, 11.3), corresponding to an absolute risk reduction of 2.3% (95% CI: 0.9, 3.8) when compared with recommending an efavirenz-based regimen for all patients and 2.6% (95% CI: 1.0, 4.2) when compared with recommending an InSTI-based regimen for all. Tailoring ART to individual patient characteristics may reduce 5-year risk of the composite outcome compared with assigning all patients the same drug regimen. [ABSTRACT FROM AUTHOR]

Studies of social networks provide unique opportunities to assess the causal effects of interventions that may impact more of the population than just those intervened on directly. Such effects are sometimes called peer or spillover effects, and may exist in the presence of interference, that is, when one individual's treatment affects another individual's outcome. Randomization‐based inference (RI) methods provide a theoretical basis for causal inference in randomized studies, even in the presence of interference. In this article, we consider RI of the intervention effect in the eX‐FLU trial, a randomized study designed to assess the effect of a social distancing intervention on influenza‐like‐illness transmission in a connected network of college students. The approach considered enables inference about the effect of the social distancing intervention on the per‐contact probability of influenza‐like‐illness transmission in the observed network. The methods allow for interference between connected individuals and for heterogeneous treatment effects. The proposed methods are evaluated empirically via simulation studies, and then applied to data from the eX‐FLU trial. [ABSTRACT FROM AUTHOR]

Interference, the dependency of an individual's potential outcome on the exposure of other individuals, is a common occurrence in medicine and public health. Recently, targeted maximum likelihood estimation (TMLE) has been extended to settings of interference, including in the context of estimation of the mean of an outcome under a specified distribution of exposure, referred to as a policy. This paper summarizes how TMLE for independent data is extended to general interference (network-TMLE). An extensive simulation study is presented of network-TMLE, consisting of four data generating mechanisms (unit-treatment effect only, spillover effects only, unit-treatment and spillover effects, infection transmission) in networks of varying structures. Simulations show that network-TMLE performs well across scenarios with interference, but issues manifest when policies are not well-supported by the observed data, potentially leading to poor confidence interval coverage. Guidance for practical application, freely available software, and areas of future work are provided. [ABSTRACT FROM AUTHOR]

In the analysis of observational studies, inverse probability weighting (IPW) is commonly used to consistently estimate the average treatment effect (ATE) or the average treatment effect in the treated (ATT). The variance of the IPW ATE estimator is often estimated by assuming that the weights are known and then using the so-called "robust" (Huber-White) sandwich estimator, which results in conservative standard errors (SEs). Here we show that using such an approach when estimating the variance of the IPW ATT estimator does not necessarily result in conservative SE estimates. That is, assuming the weights are known, the robust sandwich estimator may be either conservative or anticonservative. Thus, confidence intervals for the ATT using the robust SE estimate will not be valid, in general. Instead, stacked estimating equations which account for the weight estimation can be used to compute a consistent, closed-form variance estimator for the IPW ATT estimator. The 2 variance estimators are compared via simulation studies and in a data analysis of the association between smoking and gene expression. [ABSTRACT FROM AUTHOR]

Inverse probability of treatment weights (IPTWs) are commonly used to control for confounding when estimating causal effects of point exposures from observational data. When planning a study that will be analyzed with IPTWs, determining the required sample size for a given level of statistical power is challenging because of the effect of weighting on the variance of the estimated causal means. This paper considers the utility of the design effect to quantify the effect of weighting on the precision of causal estimates. The design effect is defined as the ratio of the variance of the causal mean estimator divided by the variance of a naïve estimator if, counter to fact, no confounding had been present and weights were not needed. A simple, closed‐form approximation of the design effect is derived that is outcome invariant and can be estimated during the study design phase. Once the design effect is approximated for each treatment group, sample size calculations are conducted as for a randomized trial, but with variances inflated by the design effects to account for weighting. Simulations demonstrate the accuracy of the design effect approximation, and practical considerations are discussed. [ABSTRACT FROM AUTHOR]

AbstractInterference occurs when a unit’s treatment (or exposure) affects another unit’s outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, that is, there is clustered interference. Various causal estimands have been proposed to quantify treatment effects under clustered interference from observational data, but these estimands either entail treatment policies lacking real-world relevance or are based on parametric propensity score models. Here, we propose new causal estimands based on modification of the propensity score distribution which may be more relevant in many contexts and are not based on parametric models. Nonparametric sample splitting estimators of the new estimands are constructed, which allow for flexible data-adaptive estimation of nuisance functions and are consistent, asymptotically normal, and efficient, converging at the usual parametric rate. Simulations show the finite sample performance of the proposed estimators. The proposed methods are applied to evaluate the effect of water, sanitation, and hygiene facilities on diarrhea among children in Senegal. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Summary: Cluster‐randomized trials are often conducted to assess vaccine effects. Defining estimands of interest before conducting a trial is integral to the alignment between a study's objectives and the data to be collected and analyzed. This paper considers estimands and estimators for overall, indirect, and total vaccine effects in trials, where clusters of individuals are randomized to vaccine or control. The scenario is considered where individuals self‐select whether to participate in the trial, and the outcome of interest is measured on all individuals in each cluster. Unlike the overall, indirect, and total effects, the direct effect of vaccination is shown in general not to be estimable without further assumptions, such as no unmeasured confounding. An illustrative example motivated by a cluster‐randomized typhoid vaccine trial is provided. [ABSTRACT FROM AUTHOR]

Assessing population‐level effects of vaccines and other infectious disease prevention measures is important to the field of public health. In infectious disease studies, one person's treatment may affect another individual's outcome, that is, there may be interference between units. For example, the use of bed nets to prevent malaria by one individual may have an indirect effect on other individuals living in close proximity. In some settings, individuals may form groups or clusters where interference only occurs within groups, that is, there is partial interference. Inverse probability weighted estimators have previously been developed for observational studies with partial interference. Unfortunately, these estimators are not well suited for studies with large clusters. Therefore, in this paper, the parametric g‐formula is extended to allow for partial interference. G‐formula estimators are proposed for overall effects, effects when treated, and effects when untreated. The proposed estimators can accommodate large clusters and do not suffer from the g‐null paradox that may occur in the absence of interference. The large sample properties of the proposed estimators are derived assuming no unmeasured confounders and that the partial interference takes a particular form (referred to as 'weak stratified interference'). Simulation studies are presented demonstrating the finite‐sample performance of the proposed estimators. The Demographic and Health Survey from the Democratic Republic of the Congo is then analyzed using the proposed g‐formula estimators to assess the effects of bed net use on malaria. [ABSTRACT FROM AUTHOR]

Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where it is a priori assumed that there is "partial interference," in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. Bowers et al. (2012, Political Anal, 21, 97–124) and Bowers et al. (2016, Political Anal, 24, 395–403) consider randomization‐based inferential methods that allow for more general interference structures in the context of randomized experiments. In this paper, extensions of Bowers et al. that allow for failure time outcomes subject to right censoring are proposed. Permitting right‐censored outcomes is challenging because standard randomization‐based tests of the null hypothesis of no treatment effect assume that whether an individual is censored does not depend on treatment. The proposed extension of Bowers et al. to allow for censoring entails adapting the method of Wang et al. (2010, Biostatistics, 11, 676–692) for two‐sample survival comparisons in the presence of unequal censoring. The methods are examined via simulation studies and utilized to assess the effects of cholera vaccination in an individually randomized trial of 73 000 children and women in Matlab, Bangladesh. [ABSTRACT FROM AUTHOR]

The United States Environmental Protection Agency considers nutrient pollution in stream ecosystems one of the United States' most pressing environmental challenges. But limited independent replicates, lack of experimental randomization, and space- and time-varying confounding handicap causal inference on effects of nutrient pollution. In this article, the causal g-methods are extended to allow for exposures to vary in time and space in order to assess the effects of nutrient pollution on chlorophyll a—a proxy for algal production. Publicly available data from North Carolina's Cape Fear River and a simulation study are used to show how causal effects of upstream nutrient concentrations on downstream chlorophyll a levels may be estimated from typical water quality monitoring data. Estimates obtained from the parametric g-formula, a marginal structural model, and a structural nested model indicate that chlorophyll a concentrations at Lock and Dam 1 were influenced by nitrate concentrations measured 86 to 109 km upstream, an area where four major industrial and municipal point sources discharge wastewater. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Dramatic decreases in HIV transmission are achievable with currently available biomedical and behavioral interventions, including antiretroviral therapy and pre-exposure prophylaxis. However, such decreases have not yet been realized among adolescents and young adults. The Adolescent Medicine Trials Network (ATN) for HIV/AIDS interventions is dedicated to research addressing the needs of youth at high risk for HIV acquisition as well as youth living with HIV. This article provides an overview of an array of efficient and effective designs across the translational spectrum that are utilized within the ATN. These designs maximize methodological rigor and real-world applicability of findings while minimizing resource use. Implementation science and cost-effectiveness methods are included. Utilizing protocol examples, we demonstrate the feasibility of such designs to balance rigor and relevance to shorten the science-to-practice gap and improve the youth HIV prevention and care continua. [ABSTRACT FROM AUTHOR]

Nonparametric bounds for the risk difference are straightforward to calculate and make no untestable assumptions about unmeasured confounding or selection bias due to missing data (e.g. dropout). These bounds are often wide and communicate uncertainty due to possible systemic errors. An illustrative example is provided. [ABSTRACT FROM AUTHOR]

Interference occurs when the treatment (or exposure) of one individual affects the outcomes of others. In some settings, it may be reasonable to assume that individuals can be partitioned into clusters such that there is no interference between individuals in different clusters, that is, there is partial interference. In observational studies with partial interference, inverse probability weighted (IPW) estimators have been something else different possible treatment effects. However, the validity of IPW estimators depends on the propensity score being known or correctly modelled. Alternatively, one can estimate the treatment effect using an outcome regression model. In this paper, we propose doubly robust (DR) estimators that utilize both models and are consistent and asymptotically normal if either model, but not necessarily both, is correctly specified. Empirical results are presented to demonstrate the DR property of the proposed estimators and the efficiency gain of DR over IPW estimators when both models are correctly specified. The different estimators are illustrated using data from a study examining the effects of cholera vaccination in Bangladesh. [ABSTRACT FROM AUTHOR]

Vaccination in populations can have several kinds of effects. Establishing that vaccination produces population-level effects beyond the direct effects in the vaccinated individuals can have important consequences for public health policy. Formal methods have been developed for study designs and analysis that can estimate the different effects of vaccination. However, implementing field studies to evaluate the different effects of vaccination can be expensive, of limited generalizability, or unethical. It would be advantageous to use routinely collected data to estimate the different effects of vaccination. We consider how different types of data are needed to estimate different effects of vaccination. The examples include rotavirus vaccination of young children, influenza vaccination of elderly adults, and a targeted influenza vaccination campaign in schools. Directions for future research are discussed. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study. [ABSTRACT FROM AUTHOR]

In both observational studies and randomized trials with noncompliance, unmeasured confounding may exist which may bias treatment effect estimates. Instrumental variables (IV) are a popular technique for addressing such confounding, enabling consistent estimation of causal effects. This paper proposes nonparametric IV estimators for censored time to event data that may be subject to competing risks. A simple, plug-in estimator is introduced using nonparametric estimators of the cumulative incidence function, with confidence intervals derived using asymptotic theory. To provide an overall test of the treatment effect, an integrated weighted difference statistic is suggested, which is applicable to data with and without competing risks. Simulation studies demonstrate that the methods perform well with realistic samples sizes. The methods are applied to assess the effect of infant or maternal antiretroviral therapy on transmission of HIV from mother to child via breastfeeding using data from a large, recently completed randomized trial in Malawi where noncompliance with assigned treatment may confound treatment effect estimates. [ABSTRACT FROM AUTHOR]

A fundamental probabilistic equivalence between randomized experiments and observational studies is presented. Given a detailed scenario, the reader is asked to consider which of two possible study designs provides more information regarding the expected difference in an outcome due to a time-fixed treatment. A general solution is described, and a particular worked example is also provided. A mathematical proof is given in the appendix. The demonstrated equivalence helps to clarify common ground between randomized experiments and observational studies, and to provide a foundation for considering both the design and interpretation of studies. [ABSTRACT FROM AUTHOR]

We consider nonparametrically estimating the joint distribution of a survival time and mark variable, where the survival time is subject to right censoring and the mark variable is only observed when the survival time is not censored. The possibility of dependent censoring is allowed for using inverse probability of censoring weights. The proposed estimator is shown to be consistent and asymptotically normal. Finite sample behavior of the proposed methods are investigated via simulation study. Finally, we illustrate the nonparametric estimator from a recent HIV vaccine efficacy trial. [ABSTRACT FROM AUTHOR]

Preclinical evaluation of candidate human immunodeficiency virus (HIV) vaccines entails challenge studies whereby non-human primates such as macaques are vaccinated with either an active or control vaccine and then challenged (exposed) with a simian-version of HIV. Repeated low-dose challenge (RLC) studies in which each macaque is challenged multiple times (either until infection or some maximum number of challenges is reached) are becoming more common in an effort to mimic natural exposure to HIV in humans. Statistical methods typically employed for the testing for a vaccine effect in RLC studies include a modified version of Fisher's exact test as well as large sample approaches such as the usual log-rank test. Unfortunately, these methods are not guaranteed to provide a valid test for the effect of vaccination. On the other hand, valid tests for vaccine effect such as the exact log-rank test may not be easy to implement using software available to many researchers. This paper details which statistical approaches are appropriate for the analysis of RLC studies, and how to implement these methods easily in SAS or R. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A critical step toward developing a successful vaccine to control the human immunodeficiency virus pandemic entails evaluation of vaccine candidates in non-human primates (NHPs). Historically, these studies have usually entailed challenges (i.e., exposures) with very high doses of a simian version of human immunodeficiency virus, resulting in infection of all NHPs in the experiment after a single challenge. More recently, researchers have begun to conduct repeated low-dose challenge (RLC) studies in NHPs that are believed to more closely mimic typical exposure in natural human transmission settings. One objective of RLC studies is to assess whether measured immune responses to vaccination can serve as surrogate endpoints for the primary endpoint of interest, namely infection. In this paper, different designs of RLC studies for assessing a binary surrogate of protection are considered. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Two methods are developed for constructing randomization-based confidence sets for the average effect of a treatment on a binary outcome. The methods are nonparametric and require no assumptions about random sampling from a larger population. Both of the resulting 1 − α confidence sets are exact in the sense that the probability of containing the true treatment effect is at least 1 − α. Both types of confidence sets are also guaranteed to have width no greater than one. In contrast, a previously proposed asymptotic confidence interval is not exact and may have width greater than 1. The first approach combines Bonferroni-adjusted prediction sets for the attributable effects in the treated and untreated. The second method entails inverting a permutation test. Simulations are presented comparing the two randomization-based confidence sets with the asymptotic interval as well as the standard Wald confidence interval and a commonly used exact interval for the difference in binomial proportions. Results show for small to moderate sample sizes that the permutation confidence set attains the narrowest width on average among the methods that maintain nominal coverage. Extensions that allow for stratifying on categorical baseline covariates are also discussed. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The epidemiologist primarily studies transitions between states of health and disease. The purpose of the present article is to define a foundational parameter for such studies, namely risk. We begin simply and build to the setting in which there is more than 1 event type (i.e., competing risks or competing events), as well as more than 1 treatment or exposure level of interest. In the presence of competing events, the risks are a set of counterfactual cumulative incidence functions for each treatment. These risks can be depicted visually and summarized numerically. We use an example from the study of human immunodeficiency virus to illustrate concepts. [ABSTRACT FROM PUBLISHER]

Interference occurs when the treatment of one person affects the outcome of another. For example, in infectious diseases, whether one individual is vaccinated may affect whether another individual becomes infected or develops disease. Quantifying such indirect (or spillover) effects of vaccination could have important public health or policy implications. In this article we use recently developed inverse-probability weighted (IPW) estimators of treatment effects in the presence of interference to analyze an individually-randomized, placebo-controlled trial of cholera vaccination that targeted 121,982 individuals in Matlab, Bangladesh. Because these IPW estimators have not been employed previously, a simulation study was also conducted to assess the empirical behavior of the estimators in settings similar to the cholera vaccine trial. Simulation study results demonstrate the IPW estimators can yield unbiased estimates of the direct, indirect, total, and overall effects of vaccination when there is interference provided the untestable no unmeasured confounders assumption holds and the group-level propensity score model is correctly specified. Application of the IPW estimators to the cholera vaccine trial indicates the presence of interference. For example, the IPW estimates suggest on average 5.29 fewer cases of cholera per 1000 person-years (95% confidence interval 2.61, 7.96) will occur among unvaccinated individuals within neighborhoods with 60% vaccine coverage compared to neighborhoods with 32% coverage. Our analysis also demonstrates how not accounting for interference can render misleading conclusions about the public health utility of vaccination. [ABSTRACT FROM AUTHOR]

In this issue of the Journal, Sullivan etal. (Am J Epidemiol. 2016;184(5):345-353) carefully examine the theoretical justification for use of the test-negative design, a common observational study design, in assessing the effectiveness of influenza vaccination. Using modern causal inference methods (in particular, directed acyclic graphs), they describe different threats to the validity of inferences drawn about the effect of vaccination from test-negative design studies. These threats include confounding, selection bias, and measurement error in either the exposure or the outcome. While confounding and measurement error are common in observational studies, the potential for selection bias inherent in the test-negative design brings into question the validity of inferences drawn from such studies. [ABSTRACT FROM AUTHOR]

Parametric estimation of the cumulative incidence function (CIF) is considered for competing risks data subject to interval censoring. Existing parametric models of the CIF for right censored competing risks data are adapted to the general case of interval censoring. Maximum likelihood estimators for the CIF are considered under the assumed models, extending earlier work on nonparametric estimation. A simple naive likelihood estimator is also considered that utilizes only part of the observed data. The naive estimator enables separate estimation of models for each cause, unlike full maximum likelihood in which all models are fit simultaneously. The naive likelihood is shown to be valid under mixed case interval censoring, but not under an independent inspection process model, in contrast with full maximum likelihood which is valid under both interval censoring models. In simulations, the naive estimator is shown to perform well and yield comparable efficiency to the full likelihood estimator in some settings. The methods are applied to data from a large, recent randomized clinical trial for the prevention of mother-to-child transmission of HIV. [ABSTRACT FROM AUTHOR]

Recently, there has been increasing interest in making causal inference when interference is possible. In the presence of interference, treatment may have several types of effects. In this article, we consider inference about such effects when the population consists of groups of individuals where interference is possible within groups but not between groups. A two-stage randomization design is assumed where in the first stage groups are randomized to different treatment allocation strategies and in the second stage individuals are randomized to treatment or control conditional on the strategy assigned to their group in the first stage. For this design, the asymptotic distributions of estimators of the causal effects are derived when either the number of individuals per group or the number of groups grows large. Under certain homogeneity assumptions, the asymptotic distributions provide justification for Wald-type confidence intervals (CIs) and tests. Empirical results demonstrate that the Wald CIs have good coverage in finite samples and are narrower than CIs based on either the Chebyshev or Hoeffding inequalities provided the number of groups is not too small. The methods are illustrated by two examples which consider the effects of cholera vaccination and an intervention to encourage voting. [ABSTRACT FROM AUTHOR]

Estimation of treatment effects in randomized studies is often hampered by possible selection bias induced by conditioning on or adjusting for a variable measured post-randomization. One approach to obviate such selection bias is to consider inference about treatment effects within principal strata, that is, principal effects. A challenge with this approach is that without strong assumptions principal effects are not identifiable from the observable data. In settings where such assumptions are dubious, identifiable large sample bounds may be the preferred target of inference. In practice these bounds may be wide and not particularly informative. In this work we consider whether bounds on principal effects can be improved by adjusting for a categorical baseline covariate. Adjusted bounds are considered which are shown to never be wider than the unadjusted bounds. Necessary and sufficient conditions are given for which the adjusted bounds will be sharper (i.e., narrower) than the unadjusted bounds. The methods are illustrated using data from a recent, large study of interventions to prevent mother-to-child transmission of HIV through breastfeeding. Using a baseline covariate indicating low birth weight, the estimated adjusted bounds for the principal effect of interest are 63% narrower than the estimated unadjusted bounds. [ABSTRACT FROM AUTHOR]

A pooled analysis of individual data from >5000 human immunodeficiency virus type 1 (HIV-1)–infected mothers and their infants from Africa and India who participated in 5 randomized trials shows that extended prophylaxis with nevirapine or with nevirapine and zidovudine significantly reduces postnatal HIV-1 infection.Background. In resource-limited settings, mothers infected with human immunodeficiency virus type 1 (HIV-1) face a difficult choice: breastfeed their infants but risk transmitting HIV-1 or not breastfeed their infants and risk the infants dying of other infectious diseases or malnutrition. Recent results from observational studies and randomized clinical trials indicate daily administration of nevirapine to the infant can prevent breast-milk HIV-1 transmission.Methods. Data from 5396 mother-infant pairs who participated in 5 randomized trials where the infant was HIV-1 negative at birth were pooled to estimate the efficacy of infant nevirapine prophylaxis to prevent breast-milk HIV-1 transmission. Four daily regimens were compared: nevirapine for 6 weeks, 14 weeks, or 28 weeks, or nevirapine plus zidovudine for 14 weeks.Results. The estimated 28-week risk of HIV-1 transmission was 5.8% (95% confidence interval [CI], 4.3%–7.9%) for the 6-week nevirapine regimen, 3.7% (95% CI, 2.5%–5.4%) for the 14-week nevirapine regimen, 4.8% (95% CI, 3.5%–6.7%) for the 14-week nevirapine plus zidovudine regimen, and 1.8% (95% CI, 1.0%–3.1%) for the 28-week nevirapine regimen (log-rank test for trend, P < .001). Cox regression models with nevirapine as a time-varying covariate, stratified by trial site and adjusted for maternal CD4 cell count and infant birth weight, indicated that nevirapine reduces the rate of HIV-1 infection by 71% (95% CI, 58%–80%; P < .001) and reduces the rate of HIV infection or death by 58% (95% CI, 45%–69%; P < .001).Conclusions. Extended prophylaxis with nevirapine or with nevirapine and zidovudine significantly reduces postnatal HIV-1 infection. Longer duration of prophylaxis results in a greater reduction in the risk of infection. [ABSTRACT FROM AUTHOR]

In randomized trials to prevent breast milk transmission of human immunodeficiency virus (HIV) from mother to infant, investigators are often interested in assessing the effect of a treatment or intervention on the cumulative risk of HIV infection by time (age) t in infants who are alive and uninfected at a certain time point τ0 < t. Such comparisons are challenging for two reasons. First, infants are typically randomized at birth (time 0 < τ0) such that comparisons between trial arms among the subset of infants alive and uninfected at τ0 are subject to selection bias. Second, in most mother-to-child transmission (MTCT) trials competing risks are often present, such as death or cessation of breastfeeding prior to HIV infection. In this paper, we present methods for assessing the causal effect of a treatment on competing risk outcomes within principal strata. In MTCT trials, the causal effect of interest is that of treatment on the risk of HIV infection by time t > τ0 within the principal stratum of infants who would be alive and uninfected by τ0 regardless of randomization assignment. We develop large sample nonparametric bounds and a semiparametric sensitivity analysis model for drawing inference about this causal effect. We present a simulation study demonstrating that the proposed methods perform well in finite samples. We apply the proposed methods to a large, recent MTCT trial. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background.Little is known about type-specific associations between prevalent human papillomavirus (HPV) infections and risk of acquiring other HPV types in men. Data on natural clustering of HPV types are needed as a prevaccine distribution to which postvaccine data can be compared.Methods.Using data from a randomized controlled trial of male circumcision in Kisumu, Kenya, adjusted mean survival ratios were estimated for acquisition of any-HPV, high-risk (HR) HPV, and individual HR-HPV types among men uninfected as compared to those infected with vaccine-relevant HPV types 16, 18, 31, 45, 6, or 11 at baseline.Results.Among 1097 human immunodeficiency virus–negative, uncircumcised men, 2303 incident HPV infections were detected over 2534 person-years of follow-up. Although acquisition of individual HR-HPV types varied by baseline HPV type, there was no clear evidence of shorter times to acquisition among men without vaccine-relevant HPV-16, -18, -31, -45, -6, or -11 infections at baseline, as compared to men who did have these infections at baseline.Conclusions.These prospective data on combinations of HPV infections over time do not suggest the potential for postvaccination HPV type replacement. Future surveillance studies are needed to definitely determine whether elimination of HPV types by vaccination will alter the HPV type distribution in the population. [ABSTRACT FROM AUTHOR]

This article examines group testing procedures where units within a group (or pool) may be correlated. The expected number of tests per unit (i.e., efficiency) of hierarchical- and matrix-based procedures is derived based on a class of models of exchangeable binary random variables. The effect on efficiency of the arrangement of correlated units within pools is then examined. In general, when correlated units are arranged in the same pool, the expected number of tests per unit decreases, sometimes substantially, relative to arrangements that ignore information about correlation. [ABSTRACT FROM AUTHOR]

Abstract: Objective: New commercial HPV RNA assays require further validation studies in population-based cervical cancer screening settings. To assess the performance of (FDA-approved) APTIMA® HPV Assay (AHPV), Hybrid Capture 2 (HC2), in-house PCR genotyping, and ThinPrep LBC in population-based screening, stratified by three histological gold standards. Study design: A multi-center trial in 5006 women undergoing routine screening in France was designed to compare the absolute and relative risks of diagnosing CIN3+ and CIN2+ lesions by different diagnostic tests. Results: Reproducibility between the primary and second pathology reading was excellent for CIN3+ and CIN2+ endpoints (Cohen''s kappa 0.948 and 0.854). Absolute risks (PPV) of different tests (AHPV, HC2, PCR genotyping, LBC) in diagnosing CIN2+ (15–20%) and CIN3+ (4–6%) were similar for the first, second, and consensus pathology readings. The relative risks of diagnosing these lesions by the four tests were also similar when the first, second or third pathology readings were employed. AHPV had the highest absolute risk of both histological endpoints, and detects 5% to 15% more CIN3+ and CIN2+ lesions, respectively, than LBC. Compared with HC2 assay, the relative risk of AHPV is 24% to 29% higher, with a significant difference in CIN2+ detection. With LBC as reference, AHPV had the best sensitivity/specificity balance measured by AUC (area under ROC curve) comparison test (significant for CIN2+), and the colposcopy referral rate (9.2%) comparable to that of LBC (8.7%). Conclusions: These data corroborate the suitability of AHPV for the primary cervical cancer screening. [Copyright &y& Elsevier]

To estimate the association of antiretroviral therapy initiation with incident acquired immunodeficiency syndrome (AIDS) or death while accounting for time-varying confounding in a cost-efficient manner, the authors combined a case-cohort study design with inverse probability-weighted estimation of a marginal structural Cox proportional hazards model. A total of 950 adults who were positive for human immunodeficiency virus type 1 were followed in 2 US cohort studies between 1995 and 2007. In the full cohort, 211 AIDS cases or deaths occurred during 4,456 person-years. In an illustrative 20% random subcohort of 190 participants, 41 AIDS cases or deaths occurred during 861 person-years. Accounting for measured confounders and determinants of dropout by inverse probability weighting, the full cohort hazard ratio was 0.41 (95% confidence interval: 0.26, 0.65) and the case-cohort hazard ratio was 0.47 (95% confidence interval: 0.26, 0.83). Standard multivariable-adjusted hazard ratios were closer to the null, regardless of study design. The precision lost with the case-cohort design was modest given the cost savings. Results from Monte Carlo simulations demonstrated that the proposed approach yields approximately unbiased estimates of the hazard ratio with appropriate confidence interval coverage. Marginal structural model analysis of case-cohort study designs provides a cost-efficient design coupled with an accurate analytic method for research settings in which there is time-varying confounding. [ABSTRACT FROM AUTHOR]

In randomized studies, treatment comparisons conditional on intermediate postrandomization outcomes using standard analytic methods do not have a causal interpretation. An alternative approach entails treatment comparisons within principal strata defined by the potential outcomes for the intermediate outcome that would be observed under each treatment assignment. In this article we develop methods for randomization-based inference within principal strata. We compare our proposed methods with existing large-sample methods as well as traditional intent-to-treat approaches. This research is motivated by HIV prevention studies, where few infections are expected and inference is desired within the always-infected principal stratum, that is, all individuals who would become infected regardless of randomization assignment. [ABSTRACT FROM AUTHOR]

Many studies have chronicled the “epidemiologic synergy” between human immunodeficiency virus (HIV) and herpes simplex virus type 2 (HSV-2). HIV adversely affects the natural history of HSV-2 and results in more frequent and severe HSV-2 reactivation. Few longitudinal studies, however, have examined whether HSV-2 is associated with increased HIV plasma viral loads or decreased CD4 counts. The authors estimated the effect of HSV-2 seropositivity on HIV RNA viral load and on CD4 count over time among 777 HIV-seropositive US women not receiving suppressive HSV-2 therapy in the HIV Epidemiology Research Study (1993–2000). Linear mixed models were used to assess the effect of HSV-2 on log HIV viral load and CD4 count/mm3 prior to widespread initiation of highly active antiretroviral therapy. Coinfection with HSV-2 was not associated with HIV RNA plasma viral loads during study follow-up. There was a statistically significant association between HSV-2 seropositivity and CD4 count over time, but this difference was small and counterintuitive at an increase of 8 cells/mm3 (95% confidence interval: 2, 14) per year among HSV-2-seropositive women compared with HSV-2-seronegative women. These data do not support a clinically meaningful effect of baseline HSV-2 seropositivity on the trajectories of HIV plasma viral loads or CD4 counts. [ABSTRACT FROM PUBLISHER]

Background: We evaluated the efficacy of a maternal triple-drug antiretroviral regimen or infant nevirapine prophylaxis for 28 weeks during breast-feeding to reduce postnatal transmission of human immunodeficiency virus type 1 (HIV-1) in Malawi. Methods: We randomly assigned 2369 HIV-1–positive, breast-feeding mothers with a CD4+ lymphocyte count of at least 250 cells per cubic millimeter and their infants to receive a maternal antiretroviral regimen, infant nevirapine, or no extended postnatal antiretroviral regimen (control group). All mothers and infants received perinatal prophylaxis with single-dose nevirapine and 1 week of zidovudine plus lamivudine. We used the Kaplan–Meier method to estimate the cumulative risk of HIV-1 transmission or death by 28 weeks among infants who were HIV-1–negative 2 weeks after birth. Rates were compared with the use of the log-rank test. Results: Among mother–infant pairs, 5.0% of infants were HIV-1–positive at 2 weeks of life. The estimated risk of HIV-1 transmission between 2 and 28 weeks was higher in the control group (5.7%) than in either the maternal-regimen group (2.9%, P=0.009) or the infant-regimen group (1.7%, P<0.001). The estimated risk of infant HIV-1 infection or death between 2 and 28 weeks was 7.0% in the control group, 4.1% in the maternal-regimen group (P=0.02), and 2.6% in the infant-regimen group (P<0.001). The proportion of women with neutropenia was higher among those receiving the antiretroviral regimen (6.2%) than among those in either the nevirapine group (2.6%) or the control group (2.3%). Among infants receiving nevirapine, 1.9% had a hypersensitivity reaction. Conclusions: The use of either a maternal antiretroviral regimen or infant nevirapine for 28 weeks was effective in reducing HIV-1 transmission during breast-feeding. (ClinicalTrials.gov number, NCT00164736.) N Engl J Med 2010;362:2271-81. [ABSTRACT FROM AUTHOR]

Evaluation of HIV vaccine candidates in nonhuman primates (NHPs) is a critical step toward developing a successful vaccine to control the HIV pandemic. Historically, HIV vaccine regimens have been tested in NHPs by administering a single high dose of the challenge virus. More recently, evaluation of candidate HIV vaccines has entailed repeated low-dose challenges, which more closely mimic typical exposure in natural transmission settings. In this article, we consider evaluation of the type and magnitude of vaccine efficacy from such experiments. Based on the principal stratification framework, we also address evaluation of potential immunological surrogate endpoints for infection. [ABSTRACT FROM AUTHOR]

We derive the operating characteristics of three-dimensional array-based testing algorithms for case identification in the presence of testing error. The operating characteristics investigated include efficiency (i.e., expected number of tests per specimen) and error rates (e.g., sensitivity, specificity, positive, and negative predictive values). The methods are illustrated by comparing the proposed algorithms with previously studied hierarchical and two-dimensional array algorithms for detecting recent HIV infections in North Carolina. Our results indicate that three-dimensional array-based algorithms can be more efficient and accurate than previously proposed algorithms in settings with test error and low prevalence. [ABSTRACT FROM AUTHOR]

Simulation studies were conducted to estimate the statistical power of repeated low-dose challenge experiments performed in nonhuman primates to detect the effect of a candidate human immunodeficiency virus vaccine. The effect of various design parameters on power was explored. Results of simulation studies indicate that repeated low-dose challenge studies with a total sample of size 50 (25 animals/arm) typically provide adequate power to detect a 50% reduction in the per-exposure probability of infection resulting from vaccination. Power generally increases with the maximum number of allowable challenges per animal, the per-exposure risk of infection in control animals, and the proportion of animals susceptible to infection. [ABSTRACT FROM AUTHOR]

The article discusses an important limitation for covariates in regression discontinuity (RD) designs. Topics discussed include weak external validity of RD designs, increasing popularity of designs, use of covariates to enhance the precision of estimated treatment effects, and limited consequences of RD designs.

Frangakis and Rubin (2002, Biometrics 58, 21–29) proposed a new definition of a surrogate endpoint (a “principal” surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case–cohort sampling of such a baseline predictor and the biomarker in a large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the “surrogate value” of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection. [ABSTRACT FROM AUTHOR]

A fundamental assumption usually made in causal inference is that of no interference between individuals (or units); that is, the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in the dependent happenings of infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this article, we consider a population of groups of individuals where interference is possible between individuals within the same group. We propose estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting. Relations among the estimands are established; for example, the total causal effect is shown to equal the sum of direct and indirect causal effects. Using an experimental design with a two-stage randomization procedure (first at the group level, then at the individual level within groups), unbiased estimators of the proposed estimands are presented. Variances of the estimators are also developed. The methodology is illustrated in two different settings where interference is likely: assessing causal effects of housing vouchers and of vaccines. [ABSTRACT FROM AUTHOR]

We derive and compare the operating characteristics of hierarchical and square array-based testing algorithms for case identification in the presence of testing error. The operating characteristics investigated include efficiency (i.e., expected number of tests per specimen) and error rates (i.e., sensitivity, specificity, positive and negative predictive values, per-family error rate, and per-comparison error rate). The methodology is illustrated by comparing different pooling algorithms for the detection of individuals recently infected with HIV in North Carolina and Malawi. [ABSTRACT FROM AUTHOR]

This article considers three nonparametric estimators of the joint distribution function for a survival time and a continuous mark variable when the survival time is interval censored and the mark variable may be missing for interval-censored observations. Finite and large sample properties are described for the nonparametric maximum likelihood estimator (NPMLE) as well as estimators based on midpoint imputation (MIDMLE) and coarsening the mark variable (CMLE). The estimators are compared using data from a simulation study and a recent phase III HIV vaccine efficacy trial where the survival time is the time from enrollment to infection and the mark variable is the genetic distance from the infecting HIV sequence to the HIV sequence in the vaccine. Theoretical and empirical evidence are presented indicating the NPMLE and MIDMLE are inconsistent. Conversely, the CMLE is shown to be consistent in general and thus is preferred. [ABSTRACT FROM AUTHOR]