HIV Prevention Among Men Who Have Sex With Men: Tenofovir Alafenamide Combination Preexposure Prophylaxis Versus Placebo.

BACKGROUND: While noninferiority of tenofovir alafenamide and emtricitabine (TAF/FTC) as preexposure prophylaxis (PrEP) for the prevention of human immunodeficiency virus (HIV) has been shown, interest remains in its efficacy relative to placebo. We estimate the efficacy of TAF/FTC PrEP versus placebo for the prevention of HIV infection. METHODS: We used data from the DISCOVER and iPrEx trials to compare TAF/FTC to placebo. DISCOVER was a noninferiority trial conducted from 2016 to 2017. iPrEx was a placebo-controlled trial conducted from 2007 to 2009. Inverse probability weights were used to standardize the iPrEx participants to the distribution of demographics and risk factors in the DISCOVER trial. To check the comparison, we evaluated whether risk of HIV infection in the shared tenofovir disoproxil fumarate and emtricitabine (TDF/FTC) arms was similar. RESULTS: Notable differences in demographics and risk factors occurred between trials. After standardization, the difference in risk of HIV infection between the TDF/FTC arms was near zero. The risk of HIV with TAF/FTC was 5.8 percentage points lower (95% confidence interval [CI], -2.0% to -9.6%) or 12.5-fold lower (95% CI, .02 to .31) than placebo standardized to the DISCOVER population. CONCLUSIONS: There was a reduction in HIV infection with TAF/FTC versus placebo across 96 weeks of follow-up. CLINICAL TRIALS REGISTRATION: NCT02842086 and NCT00458393.

Transportability Without Positivity: A Synthesis of Statistical and Simulation Modeling.

Studies designed to estimate the effect of an action in a randomized or observational setting often do not represent a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions is ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches was able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge.

Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters.

Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods.

Fusing trial data for treatment comparisons: Single vs multi-span bridging.

While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from trial data. RCTs are limited to a small number of comparator arms and often compare a new therapeutic to a standard of care which has already proven efficacious. It is sometimes of interest to estimate the efficacy of the new therapy relative to a treatment that was not evaluated in the same trial, such as a placebo or an alternative therapy that was evaluated in a different trial. Such dual-study comparisons are challenging because of potential differences between trial populations that can affect the outcome. In this article, two bridging estimators are considered that allow for comparisons of treatments evaluated in different trials, accounting for measured differences in trial populations. A "multi-span" estimator leverages a shared arm between two trials, while a "single-span" estimator does not require a shared arm. A diagnostic statistic that compares the outcome in the standardized shared arms is provided. The two estimators are compared in simulations, where both estimators demonstrate minimal empirical bias and nominal confidence interval coverage when the identification assumptions are met. The estimators are applied to data from the AIDS Clinical Trials Group 320 and 388 to compare the efficacy of two-drug vs four-drug antiretroviral therapy on CD4 cell counts among persons with advanced HIV. The single-span approach requires weaker identification assumptions and was more efficient in simulations and the application.

Higher-order evidence.

Higher-order evidence is evidence about evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or outcome collected in the main study. While sampling variability in higher-order evidence is typically acknowledged, higher-order evidence is often assumed to be free of measurement error (e.g., gold standard measures). Here we provide two examples, each with multiple scenarios where second-order evidence is imperfectly measured, and this measurement error can either amplify or attenuate standard corrections to first-order evidence. We propose a way to account for such imperfections that requires third-order evidence. Further illustrations and exploration of how higher-order evidence impacts results of epidemiologic studies is warranted.

Evaluating Clinic-Based Interventions to Reduce Racial Differences in Mortality Among People With Human Immunodeficiency Virus in the United States.

BACKGROUND: Mortality remains elevated among Black versus White adults receiving human immunodeficiency virus (HIV) care in the United States. We evaluated the effects of hypothetical clinic-based interventions on this mortality gap. METHODS: We computed 3-year mortality under observed treatment patterns among >40 000 Black and >30 000 White adults entering HIV care in the United States from 1996 to 2019. We then used inverse probability weights to impose hypothetical interventions, including immediate treatment and guideline-based follow-up. We considered 2 scenarios: "universal" delivery of interventions to all patients and "focused" delivery of interventions to Black patients while White patients continued to follow observed treatment patterns. RESULTS: Under observed treatment patterns, 3-year mortality was 8% among White patients and 9% among Black patients, for a difference of 1 percentage point (95% confidence interval [CI], .5-1.4). The difference was reduced to 0.5% under universal immediate treatment (95% CI, -.4% to 1.3%) and to 0.2% under universal immediate treatment combined with guideline-based follow-up (95% CI, -1.0% to 1.4%). Under the focused delivery of both interventions to Black patients, the Black-White difference in 3-year mortality was -1.4% (95% CI, -2.3% to -.4%). CONCLUSIONS: Clinical interventions, particularly those focused on enhancing the care of Black patients, could have significantly reduced the mortality gap between Black and White patients entering HIV care from 1996 to 2019.

Illustration of 2 Fusion Designs and Estimators.

"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information.

Missing Outcome Data in Epidemiologic Studies.

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology.

A Per-Protocol Analysis Using Inverse-Probability-of-Censoring Weights in a Randomized Trial of Initial Protease Inhibitor Versus Nonnucleoside Reverse Transcriptase Inhibitor Regimens in Children.

Protocol adherence may influence measured treatment effectiveness in randomized controlled trials. Using data from a multicenter trial (Europe and the Americas, 2002-2009) of children with human immunodeficiency virus type 1 who had been randomized to receive initial protease inhibitor (PI) versus nonnucleoside reverse transcriptase inhibitor (NNRTI) antiretroviral therapy regimens, we generated time-to-event intention-to-treat (ITT) estimates of treatment effectiveness, applied inverse-probability-of-censoring weights to generate per-protocol efficacy estimates, and compared shifts from ITT to per-protocol estimates across and within treatment arms. In ITT analyses, 263 participants experienced 4-year treatment failure probabilities of 41.3% for PIs and 39.5% for NNRTIs (risk difference = 1.8% (95% confidence interval (CI): -10.1, 13.7); hazard ratio = 1.09 (95% CI: 0.74, 1.60)). In per-protocol analyses, failure probabilities were 35.6% for PIs and 29.2% for NNRTIs (risk difference = 6.4% (95% CI: -6.7, 19.4); hazard ratio = 1.30 (95% CI: 0.80, 2.12)). Within-arm shifts in failure probabilities from ITT to per-protocol analyses were 5.7% for PIs and 10.3% for NNRTIs. Protocol nonadherence was nondifferential across arms, suggesting that possibly better NNRTI efficacy may have been masked by differences in within-arm shifts deriving from differential regimen forgiveness, residual confounding, or chance. A per-protocol approach using inverse-probability-of-censoring weights facilitated evaluation of relationships among adherence, efficacy, and forgiveness applicable to pediatric oral antiretroviral regimens.

Optimizing Treatment for Human Immunodeficiency Virus to Improve Clinical Outcomes Using Precision Medicine.

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.

When Does Differential Outcome Misclassification Matter for Estimating Prevalence?

BACKGROUND: When accounting for misclassification, investigators make assumptions about whether misclassification is "differential" or "nondifferential." Most guidance on differential misclassification considers settings where outcome misclassification varies across levels of exposure, or vice versa. Here, we examine when covariate-differential misclassification must be considered when estimating overall outcome prevalence. METHODS: We generated datasets with outcome misclassification under five data generating mechanisms. In each, we estimated prevalence using estimators that (a) ignored misclassification, (b) assumed misclassification was nondifferential, and (c) allowed misclassification to vary across levels of a covariate. We compared bias and precision in estimated prevalence in the study sample and an external target population using different sources of validation data to account for misclassification. We illustrated use of each approach to estimate HIV prevalence using self-reported HIV status among people in East Africa cross-border areas. RESULTS: The estimator that allowed misclassification to vary across levels of the covariate produced results with little bias for both populations in all scenarios but had higher variability when the validation study contained sparse strata. Estimators that assumed nondifferential misclassification produced results with little bias when the covariate distribution in the validation data matched the covariate distribution in the target population; otherwise estimates assuming nondifferential misclassification were biased. CONCLUSIONS: If validation data are a simple random sample from the target population, assuming nondifferential outcome misclassification will yield prevalence estimates with little bias regardless of whether misclassification varies across covariates. Otherwise, obtaining valid prevalence estimates requires incorporating covariates into the estimators used to account for misclassification.

Sensitivity Analyses for Means or Proportions with Missing Outcome Data.

We describe an approach to sensitivity analysis introduced by Robins et al (1999), for the setting where the outcome is missing for some observations. This flexible approach focuses on the relationship between the outcomes and missingness, where data can be missing completely at random, missing at random given observed data, or missing not at random. We provide examples from HIV that include the sensitivity of the estimation of a mean and proportion under different missingness mechanisms. The approach illustrated provides a method for examining how the results of epidemiologic studies might shift as a function of bias due to missing data.

Disparities in Fatal Occupational Injury Rates in North Carolina, 1978-2017: Comparing Nonmanagerial Employees to Managers.

BACKGROUND: We examined fatal occupational injuries among private-sector workers in North Carolina during the 40-year period 1978-2017, comparing the occurrence of fatal injuries among nonmanagerial employees to that experienced by managers. METHODS: We estimated a standardized fatal occupational injury ratio by inverse probability of exposure weighting, taking nonmanagerial workers as the target population. When this ratio measure takes a value greater than unity it signals settings in which nonmanagerial employees are not provided as safe a work environment as that provided for managers. RESULTS: Across all industries, nonmanagerial workers in North Carolina experienced fatal occupational injury rates 8.2 (95% CI = 7.0, 10.0) times the rate experienced by managers. Disparities in fatal injury rates between managers and the employees they supervise were greatest in forestry, rubber and metal manufacturing, wholesale trade, fishing and extractive industries, and construction. CONCLUSIONS: The results may help focus discussion about workplace safety between labor and management upon equity, with a goal of providing a work environment for nonmanagerial employees as safe as the one provided for managers.

Accounting for nonmonotone missing data using inverse probability weighting.

Inverse probability weighting can be used to correct for missing data. New estimators for the weights in the nonmonotone setting were introduced in 2018. These estimators are the unconstrained maximum likelihood estimator (UMLE) and the constrained Bayesian estimator (CBE), an alternative if UMLE fails to converge. In this work we describe and illustrate these estimators, and examine performance in simulation and in an applied example estimating the effect of anemia on spontaneous preterm birth in the Zambia Preterm Birth Prevention Study. We compare performance with multiple imputation (MI) and focus on the setting of an observational study where inverse probability of treatment weights are used to address confounding. In simulation, weighting was less statistically efficient at the smallest sample size and lowest exposure prevalence examined (n = 1500, 15% respectively) but in other scenarios statistical performance of weighting and MI was similar. Weighting had improved computational efficiency taking, on average, 0.4 and 0.05 times the time for MI in R and SAS, respectively. UMLE was easy to implement in commonly used software and convergence failure occurred just twice in >200 000 simulated cohorts making implementation of CBE unnecessary. In conclusion, weighting is an alternative to MI for nonmonotone missingness, though MI performed as well as or better in terms of bias and statistical efficiency. Weighting's superior computational efficiency may be preferred with large sample sizes or when using resampling algorithms. As validity of weighting and MI rely on correct specification of different models, both approaches could be implemented to check agreement of results.

Association of Race and Ethnicity With Initial Prescription of Antiretroviral Therapy Among People With HIV in the US.

Importance: Integrase strand transfer inhibitor (INSTI)-containing antiretroviral therapy (ART) is currently the guideline-recommended first-line treatment for HIV. Delayed prescription of INSTI-containing ART may amplify differences and inequities in health outcomes. Objectives: To estimate racial and ethnic differences in the prescription of INSTI-containing ART among adults newly entering HIV care in the US and to examine variation in these differences over time in relation to changes in treatment guidelines. Design, Setting, and Participants: Retrospective observational study of 42 841 adults entering HIV care from October 12, 2007, when the first INSTI was approved by the US Food and Drug Administration, to April 30, 2019, at more than 200 clinical sites contributing to the North American AIDS Cohort Collaboration on Research and Design. Exposures: Combined race and ethnicity as reported in patient medical records. Main Outcomes and Measures: Probability of initial prescription of ART within 1 month of care entry and probability of being prescribed INSTI-containing ART. Differences among non-Hispanic Black and Hispanic patients compared with non-Hispanic White patients were estimated by calendar year and time period in relation to changes in national guidelines on the timing of treatment initiation and recommended initial treatment regimens. Results: Of 41 263 patients with information on race and ethnicity, 19 378 (47%) as non-Hispanic Black, 6798 (16%) identified as Hispanic, and 13 539 (33%) as non-Hispanic White; 36 394 patients (85%) were male, and the median age was 42 years (IQR, 30 to 51). From 2007-2015, when guidelines recommended treatment initiation based on CD4+ cell count, the probability of ART initiation within 1 month of care entry was 45% among White patients, 45% among Black patients (difference, 0% [95% CI, -1% to 1%]), and 51% among Hispanic patients (difference, 5% [95% CI, 4% to 7%]). From 2016-2019, when guidelines strongly recommended treating all patients regardless of CD4+ cell count, this probability increased to 66% among White patients, 68% among Black patients (difference, 2% [95% CI, -1% to 5%]), and 71% among Hispanic patients (difference, 5% [95% CI, 1% to 9%]). INSTIs were prescribed to 22% of White patients and only 17% of Black patients (difference, -5% [95% CI, -7% to -4%]) and 17% of Hispanic patients (difference, -5% [95% CI, -7% to -3%]) from 2009-2014, when INSTIs were approved as initial therapy but were not yet guideline recommended. Significant differences persisted for Black patients (difference, -6% [95% CI, -8% to -4%]) but not for Hispanic patients (difference, -1% [95% CI, -4% to 2%]) compared with White patients from 2014-2017, when INSTI-containing ART was a guideline-recommended option for initial therapy; differences by race and ethnicity were not statistically significant from 2017-2019, when INSTI-containing ART was the single recommended initial therapy for most people with HIV. Conclusions and Relevance: Among adults entering HIV care within a large US research consortium from 2007-2019, the 1-month probability of ART prescription was not significantly different across most races and ethnicities, although Black and Hispanic patients were significantly less likely than White patients to receive INSTI-containing ART in earlier time periods but not after INSTIs became guideline-recommended initial therapy for most people with HIV. Additional research is needed to understand the underlying racial and ethnic differences and whether the differences in prescribing were associated with clinical outcomes.

Five-Year Mortality for Adults Entering Human Immunodeficiency Virus Care Under Universal Early Treatment Compared With the General US Population.

BACKGROUND: Mortality among adults with human immunodeficiency virus (HIV) remains elevated over those in the US general population, even in the years after entry into HIV care. We explore whether the elevation in 5-year mortality would have persisted if all adults with HIV had initiated antiretroviral therapy within 3 months of entering care. METHODS: Among 82 766 adults entering HIV care at North American AIDS Cohort Collaboration clinical sites in the United States, we computed mortality over 5 years since entry into HIV care under observed treatment patterns. We then used inverse probability weights to estimate mortality under universal early treatment. To compare mortality with those for similar individuals in the general population, we used National Center for Health Statistics data to construct a cohort representing the subset of the US population matched to study participants on key characteristics. RESULTS: For the entire study period (1999-2017), the 5-year mortality among adults with HIV was 7.9% (95% confidence interval [CI]: 7.6%-8.2%) higher than expected based on the US general population. Under universal early treatment, the elevation in mortality for people with HIV would have been 7.2% (95% CI: 5.8%-8.6%). In the most recent calendar period examined (2011-2017), the elevation in mortality for people with HIV was 2.6% (95% CI: 2.0%-3.3%) under observed treatment patterns and 2.1% (.0%-4.2%) under universal early treatment. CONCLUSIONS: Expanding early treatment may modestly reduce, but not eliminate, the elevation in mortality for people with HIV.

Amplification of Bias Due to Exposure Measurement Error.

Observational epidemiologic studies typically face challenges of exposure measurement error and confounding. Consider an observational study of the association between a continuous exposure and an outcome, where the exposure variable of primary interest suffers from classical measurement error (i.e., the measured exposures are distributed around the true exposure with independent error). In the absence of exposure measurement error, it is widely recognized that one should control for confounders of the association of interest to obtain an unbiased estimate of the effect of that exposure on the outcome of interest. However, here we show that, in the presence of classical exposure measurement error, the net bias in an estimate of the association of interest may increase upon adjustment for confounders. We offer an analytical expression for calculating the change in net bias in an estimate of the association of interest upon adjustment for a confounder in the presence of classical exposure measurement error, and we illustrate this problem using simulations.

Estimating the Complier Average Causal Effect in a Meta-Analysis of Randomized Clinical Trials With Binary Outcomes Accounting for Noncompliance: A Generalized Linear Latent and Mixed Model Approach.

Noncompliance, a common problem in randomized clinical trials (RCTs), can bias estimation of the effect of treatment receipt using a standard intention-to-treat analysis. The complier average causal effect (CACE) measures the effect of an intervention in the latent subpopulation that would comply with their assigned treatment. Although several methods have been developed to estimate the CACE in analyzing a single RCT, methods for estimating the CACE in a meta-analysis of RCTs with noncompliance await further development. This article reviews the assumptions needed to estimate the CACE in a single RCT and proposes a frequentist alternative for estimating the CACE in a meta-analysis, using a generalized linear latent and mixed model with SAS software (SAS Institute, Inc.). The method accounts for between-study heterogeneity using random effects. We implement the methods and describe an illustrative example of a meta-analysis of 10 RCTs evaluating the effect of receiving epidural analgesia in labor on cesarean delivery, where noncompliance varies dramatically between studies. Simulation studies are used to evaluate the performance of the proposed method.

A Bespoke Instrumental Variable Approach to Correction for Exposure Measurement Error.

A covariate-adjusted estimate of an exposure-outcome association may be biased if the exposure variable suffers measurement error. We propose an approach to correct for exposure measurement error in a covariate-adjusted estimate of the association between a continuous exposure variable and outcome of interest. Our proposed approach requires data for a reference population in which the exposure was a priori set to some known level (e.g., 0, and is therefore unexposed); however, our approach does not require an exposure validation study or replicate measures of exposure, which are typically needed when addressing bias due to exposure measurement error. A key condition for this method, which we refer to as "partial population exchangeability," requires that the association between a measured covariate and outcome in the reference population equals the association between that covariate and outcome in the target population in the absence of exposure. We illustrate the approach using simulations and an example.

Toward a Clearer Definition of Selection Bias When Estimating Causal Effects.

Selection bias remains a subject of controversy. Existing definitions of selection bias are ambiguous. To improve communication and the conduct of epidemiologic research focused on estimating causal effects, we propose to unify the various existing definitions of selection bias in the literature by considering any bias away from the true causal effect in the referent population (the population before the selection process), due to selecting the sample from the referent population, as selection bias. Given this unified definition, selection bias can be further categorized into two broad types: type 1 selection bias owing to restricting to one or more level(s) of a collider (or a descendant of a collider) and type 2 selection bias owing to restricting to one or more level(s) of an effect measure modifier. To aid in explaining these two types-which can co-occur-we start by reviewing the concepts of the target population, the study sample, and the analytic sample. Then, we illustrate both types of selection bias using causal diagrams. In addition, we explore the differences between these two types of selection bias, and describe methods to minimize selection bias. Finally, we use an example of "M-bias" to demonstrate the advantage of classifying selection bias into these two types.