Real-world Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents.

Background: The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited. Objective: To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents. Design: Comparative effectiveness research accounting for underreported vaccination in three study cohorts: adolescents (12 to 20 years) during the Delta phase, children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase. Setting: A national collaboration of pediatric health systems (PEDSnet). Participants: 77,392 adolescents (45,007 vaccinated) in the Delta phase, 111,539 children (50,398 vaccinated) and 56,080 adolescents (21,180 vaccinated) in the Omicron period. Exposures: First dose of the BNT162b2 vaccine vs. no receipt of COVID-19 vaccine. Measurements: Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100% with confounders balanced via propensity score stratification. Results: During the Delta period, the estimated effectiveness of BNT162b2 vaccine was 98.4% (95% CI, 98.1 to 98.7) against documented infection among adolescents, with no significant waning after receipt of the first dose. An analysis of cardiac complications did not find an increased risk after vaccination. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (95% CI, 72.2 to 76.2). Higher levels of effectiveness were observed against moderate or severe COVID-19 (75.5%, 95% CI, 69.0 to 81.0) and ICU admission with COVID-19 (84.9%, 95% CI, 64.8 to 93.5). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (95% CI, 83.8 to 87.1), with 84.8% (95% CI, 77.3 to 89.9) against moderate or severe COVID-19, and 91.5% (95% CI, 69.5 to 97.6)) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined after 4 months following the first dose and then stabilized. The analysis revealed a lower risk of cardiac complications in the vaccinated group during the Omicron variant period. Limitations: Observational study design and potentially undocumented infection. Conclusions: Our study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time. Primary Funding Source: National Institutes of Health.

Structural cumulative survival models for estimation of treatment effects accounting for treatment switching in randomized experiments.

Treatment switching in a randomized controlled trial occurs when a patient in one treatment arm switches to another arm during follow-up. This can occur at the point of disease progression, whereby patients in the control arm may be offered the experimental treatment. It is widely known that failure to account for treatment switching can seriously bias the estimated treatment causal effect. In this paper, we aim to account for the potential impact of treatment switching in a reanalysis evaluating the treatment effect of nucleoside reverse transcriptase inhibitors (NRTIs) on a safety outcome (time to first severe or worse sign or symptom) in participants receiving a new antiretroviral regimen that either included or omitted NRTIs in the optimized treatment that includes or omits NRTIs trial. We propose an estimator of a treatment causal effect for a censored time to event outcome under a structural cumulative survival model that leverages randomization as an instrumental variable to account for selective treatment switching. We establish that the proposed estimator is uniformly consistent and asymptotically Gaussian, with a consistent variance estimator and confidence intervals given, whose finite-sample performance is evaluated via extensive simulations. An R package 'ivsacim' implementing all proposed methods is freely available on R CRAN. Results indicate that adding NRTIs versus omitting NRTIs to a new optimized treatment regime may increase the risk for a safety outcome.

Multiply robust causal inference with double-negative control adjustment for categorical unmeasured confounding.

Unmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long-standing tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao and colleagues have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to identify non-parametrically the average treatment effect (ATE) from observational data subject to uncontrolled confounding. We establish non-parametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables. We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the non-parametric model, and we propose multiply robust and locally efficient estimators when non-parametric estimation may not be feasible. We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the post-licensure surveillance of vaccine safety among children.

A stochastic second-order generalized estimating equations approach for estimating association parameters.

Design and analysis of cluster randomized trials must take into account the intraclass correlation coefficient (ICC), which quantifies the correlation among outcomes from the same cluster. Second-order generalized estimating equations (GEE2) provides a statistically robust way in estimating this quantity and other association parameters. However, GEE2 becomes computationally infeasible as cluster sizes grow. This paper proposes a stochastic variant to fitting GEE2 which alleviates reliance on parameter starting values and provides substantially faster speeds and higher convergence rates than the widely used deterministic Newton-Raphson method. We also propose new estimators for the ICC which account for informative missing outcome data through the use of GEE2, for which we incorporate a "second-order" inverse probability weighting scheme and "second-order" doubly robust (DR) estimating equations that guard against partial model misspecification. Our proposed methods are evaluated through simulations and applied to data from a cluster randomized trial in Bangladesh evaluating the effect of different marketing interventions on the use of hygienic latrines.

Implementation of Instrumental Variable Bounds for Data Missing Not at Random.

Instrumental variables are routinely used to recover a consistent estimator of an exposure causal effect in the presence of unmeasured confounding. Instrumental variable approaches to account for nonignorable missing data also exist but are less familiar to epidemiologists. Like instrumental variables for exposure causal effects, instrumental variables for missing data rely on exclusion restriction and instrumental variable relevance assumptions. Yet these two conditions alone are insufficient for point identification. For estimation, researchers have invoked a third assumption, typically involving fairly restrictive parametric constraints. Inferences can be sensitive to these parametric assumptions, which are typically not empirically testable. The purpose of our article is to discuss another approach for leveraging a valid instrumental variable. Although the approach is insufficient for nonparametric identification, it can nonetheless provide informative inferences about the presence, direction, and magnitude of selection bias, without invoking a third untestable parametric assumption. An important contribution of this article is an Excel spreadsheet tool that can be used to obtain empirical evidence of selection bias and calculate bounds and corresponding Bayesian 95% credible intervals for a nonidentifiable population proportion. For illustrative purposes, we used the spreadsheet tool to analyze HIV prevalence data collected by the 2007 Zambia Demographic and Health Survey (DHS).

Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference.

Nonmonotone missing data arise routinely in empirical studies of social and health sciences, and when ignored, can induce selection bias and loss of efficiency. In practice, it is common to account for nonresponse under a missing-at-random assumption which although convenient, is rarely appropriate when nonresponse is nonmonotone. Likelihood and Bayesian missing data methodologies often require specification of a parametric model for the full data law, thus a priori ruling out any prospect for semiparametric inference. In this paper, we propose an all-purpose approach which delivers semiparametric inferences when missing data are nonmonotone and not at random. The approach is based on a discrete choice model (DCM) as a means to generate a large class of nonmonotone nonresponse mechanisms that are nonignorable. Sufficient conditions for nonparametric identification are given, and a general framework for fully parametric and semiparametric inference under an arbitrary DCM is proposed. Special consideration is given to the case of logit discrete choice nonresponse model (LDCM) for which we describe generalizations of inverse-probability weighting, pattern-mixture estimation, doubly robust estimation and multiply robust estimation.

Quantifying an Adherence Path-Specific Effect of Antiretroviral Therapy in the Nigeria PEPFAR Program.

Since the early 2000s, evidence has accumulated for a significant differential effect of first-line antiretroviral therapy (ART) regimens on human immunodeficiency virus (HIV) viral load suppression. This finding was replicated in our data from the Harvard President's Emergency Plan for AIDS Relief (PEPFAR) program in Nigeria. Investigators were interested in finding the source of these differences, i.e., understanding the mechanisms through which one regimen outperforms another, particularly via adherence. This question can be naturally formulated via mediation analysis with adherence playing the role of a mediator. Existing mediation analysis results, however, have relied on an assumption of no exposure-induced confounding of the intermediate variable, and generally require an assumption of no unmeasured confounding for nonparametric identification. Both assumptions are violated by the presence of drug toxicity. In this paper, we relax these assumptions and show that certain path-specific effects remain identified under weaker conditions. We focus on the path-specific effect solely mediated by adherence and not by toxicity and propose an estimator for this effect. We illustrate with simulations and present results from a study applying the methodology to the Harvard PEPFAR data. Supplementary materials are available online.

Marginalized zero-altered models for longitudinal count data.

Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias.

Botswana's progress toward achieving the 2020 UNAIDS 90-90-90 antiretroviral therapy and virological suppression goals: a population-based survey.

BACKGROUND: HIV programmes face challenges achieving high rates of HIV testing and treatment needed to optimise health and to reduce transmission. We used data from the Botswana Combination Prevention Project study survey to assess Botswana's progress toward achieving UNAIDS targets for 2020: 90% of all people living with HIV knowing their status, 90% of these receiving sustained antiretroviral therapy (ART), and 90% of those having virological suppression (90-90-90). METHODS: A population-based sample of individuals was recruited and interviewed in 30 rural and periurban communities from Oct 30, 2013, to Nov 24, 2015, as part of a large, ongoing community-randomised trial designed to assess the effect of a combination prevention package on HIV incidence. A random sample of about 20% of households in each community was selected. Consenting household residents aged 16-64 years who were Botswana citizens or spouses of citizens responded to a questionnaire and had blood drawn for HIV testing in the absence of documentation of positive HIV status. Viral load testing was done in all HIV-infected participants, irrespective of treatment status. We used modified Poisson generalised estimating equations to obtain prevalence ratios, corresponding Huber robust SEs, and 95% Wald CIs to examine associations between individual sociodemographic factors and a binary outcome indicating achievement of the three individual and combined overall 90-90-90 targets. The study is registered at ClinicalTrials.gov, number NCT01965470. FINDINGS: 81% of enumerated eligible household members took part in the survey (10% refused and 9% were absent). Among 12 610 participants surveyed, 3596 (29%) were infected with HIV, and 2995 (83·3%, 95% CI 81·4-85·2) of these individuals already knew their HIV status. Among those who knew their HIV status, 2617 (87·4%, 95% CI 85·8-89·0) were receiving ART (95% of those eligible by national guidelines, and 73% of all infected people). Of the 2609 individuals receiving ART with a viral load measurement, 2517 (96·5%, 95% CI 96·0-97·0) had viral load of 400 copies per mL or less. Overall, 70·2% (95% CI 67·5-73·0) of HIV-infected people had virological suppression, close to the UNAIDS target of 73%. INTERPRETATION: UNAIDS 90-90-90 targets are achievable even in resource-constrained settings with high HIV burden. FUNDING: US President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention.

Blood pressure and cognition: factors that may account for their inconsistent association.

BACKGROUND: Studies of hypertension and cognition variously report adverse, null, and protective associations. We evaluated evidence supporting three potential explanations for this variation: an effect of hypertension duration, an effect of age at hypertension initiation, and selection bias due to dependent censoring. METHODS: The Normative Aging Study is a prospective cohort study of men in the greater Boston area. Participants completed study visits, including hypertension assessment, every 3-5 years starting in 1961. Seven hundred fifty-eight of 1,284 men eligible at baseline completed cognitive assessment between 1992 and 2005; we used the mean age-adjusted cognitive test Z score from their first assessment to quantify cognition. We estimated how becoming hypertensive and increasing age at onset and duration since hypertension initiation affect cognition. We used inverse probability of censoring weights to reduce and quantify selection bias. RESULTS: A history of hypertension diagnosis predicted lower cognition. Increasing duration since hypertension initiation predicted lower mean cognitive Z score (-0.02 standard units per year increase [95% confidence interval= -0.04 to -0.001]), independent of age at onset. Comparing participants with and without hypertension, we observed noteworthy differences in mean cognitive score only for those with a long duration since hypertension initiation, regardless of age at onset. Age at onset was not associated with cognition independent of duration. Analyses designed to quantify selection bias suggested upward bias. CONCLUSIONS: Previous findings of null or protective associations between hypertension and cognition likely reflect the study of persons with short duration since hypertension initiation. Selection bias may also contribute to cross-study heterogeneity.

Differential mental health effects of neighborhood relocation among youth in vulnerable families: results from a randomized trial.

CONTEXT Extensive observational evidence indicates that youth in high-poverty neighborhoods exhibit poor mental health, although not all children may be affected similarly. OBJECTIVE To use experimental evidence to assess whether gender and family health problems modify the mental health effects of moving from high- to low-poverty neighborhoods. DESIGN Randomized controlled trial. SETTING Volunteer low-income families in public housing in 5 US cities between 1994-1997. PARTICIPANTS We analyze 4- to 7-year outcomes in youth aged 12 to 19 years (n = 2829, 89% effective response rate) in the Moving to Opportunity Study. INTERVENTION Families were randomized to remain in public housing (control group) or to receive government-funded rental subsidies to move into private apartments (experimental group). Intention-to-treat analyses included intervention interactions by gender and health vulnerability (defined as prerandomization health/developmental limitations or disabilities in family members). MAIN OUTCOME MEASURES Past-year psychological distress (Kessler 6 scale [K6]) and the Behavioral Problems Index (BPI). Supplemental analyses used past-year major depressive disorder (MDD). RESULTS Male gender (P = .02) and family health vulnerability (P = .002) significantly adversely modified the intervention effect on K6 scores; male gender (P = .01), but not health vulnerability (P = .17), significantly adversely modified the intervention effect on the BPI. Girls without baseline health vulnerabilities were the only subgroup to benefit on any outcome (K6: ß = -0.21; 95% CI, -0.34 to -0.07; P = .003; MDD: odds ratio = 0.42; 95% CI, 0.20 to 0.85; P = .02). For boys with health vulnerabilities, intervention was associated with worse K6 (ß = 0.26; 95% CI, 0.09 to 0.44; P = .003) and BPI (ß = 0.24; 95% CI, 0.09 to 0.40; P = .002) values. Neither girls with health vulnerability nor boys without health vulnerability experienced intervention benefits. Adherence-adjusted instrumental variable analysis found intervention effects twice as large. Patterns were similar for MDD, but estimates were imprecise owing to low prevalence. CONCLUSIONS Although some girls benefited, boys and adolescents from families with baseline health problems did not experience mental health benefits from housing mobility policies and may need additional program supports.

A mapping between interactions and interference: implications for vaccine trials.

In this paper, we discuss relationships between causal interactions within the counterfactual framework and interference in which the exposure of one person may affect the outcomes of another. We show that the empirical tests for causal interactions can, in fact, all be adapted to empirical tests for particular forms of interference. In the context of interference, by recoding the response as some function of the outcomes of the various persons within a cluster, a wide range of different forms of interference can potentially be detected. The correspondence between causal interactions and forms of interference extends to encompass n-way causal interactions, interference between n persons within a cluster, and multivalued exposures. The theory for causal interactions provides a complete conceptual apparatus for assessing interference as well. The results are illustrated using data from a hypothetical vaccine trial to reason about specific forms of interference and spillover effects that may be present in this vaccine setting. We discuss the implications of this correspondence for our conceptualizations of interaction and for application to vaccine trials and many other settings in which spillover effects may be present.

Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis.

Whilst estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon evaluating the total effect of the exposure, investigators routinely wish to make inferences about the direct or indirect pathways of the effect of the exposure not through or through a mediator variable that occurs subsequently to the exposure and prior to the outcome. Although powerful semiparametric methodologies have been developed to analyze observational studies, that produce double robust and highly efficient estimates of the marginal total causal effect, similar methods for mediation analysis are currently lacking. Thus, this paper develops a general semiparametric framework for obtaining inferences about so-called marginal natural direct and indirect causal effects, while appropriately accounting for a large number of pre-exposure confounding factors for the exposure and the mediator variables. Our analytic framework is particularly appealing, because it gives new insights on issues of efficiency and robustness in the context of mediation analysis. In particular, we propose new multiply robust locally efficient estimators of the marginal natural indirect and direct causal effects, and develop a novel double robust sensitivity analysis framework for the assumption of ignorability of the mediator variable.

Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes.

The question of which statistical approach is the most effective for investigating gene-environment (G-E) interactions in the context of genome-wide association studies (GWAS) remains unresolved. By using 2 case-control GWAS (the Nurses' Health Study, 1976-2006, and the Health Professionals Follow-up Study, 1986-2006) of type 2 diabetes, the authors compared 5 tests for interactions: standard logistic regression-based case-control; case-only; semiparametric maximum-likelihood estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests. The authors also compared 2 joint tests of genetic main effects and G-E interaction. Elevated body mass index was the exposure of interest and was modeled as a binary trait to avoid an inflated type I error rate that the authors observed when the main effect of continuous body mass index was misspecified. Although both the case-only and the semiparametric maximum-likelihood estimation approaches assume that the tested markers are independent of exposure in the general population, the authors did not observe any evidence of inflated type I error for these tests in their studies with 2,199 cases and 3,044 controls. Both joint tests detected markers with known marginal effects. Loci with the most significant G-E interactions using the standard, empirical-Bayes, and 2-stage tests were strongly correlated with the exposure among controls. Study findings suggest that methods exploiting G-E independence can be efficient and valid options for investigating G-E interactions in GWAS.

The semiparametric case-only estimator.

We propose a semiparametric case-only estimator of multiplicative gene-environment or gene-gene interactions, under the assumption of conditional independence of the two factors given a vector of potential confounding variables. Our estimator yields valid inferences on the interaction function if either but not necessarily both of two unknown baseline functions of the confounders is correctly modeled. Furthermore, when both models are correct, our estimator has the smallest possible asymptotic variance for estimating the interaction parameter in a semiparametric model that assumes that at least one but not necessarily both baseline models are correct.