Interest in and Preference for Long-acting Injectable Antiretroviral Therapy in the Era of Approved Cabotegravir/Rilpivirine among Reproductive-aged Women in the U.S. South.

Among 103 reproductive-aged women with HIV in the U.S. South surveyed post-approval of long-acting injectable (LAI) cabotegravir/rilpivirine, nearly two-thirds reported willingness to try LAI antiretroviral therapy (ART). Most expressed preference for LAI over daily oral ART and had minimal concerns over potential LAI-ART use impacting reproductive health.

Selection Bias Requires Selection: The Case of Collider Stratification Bias.

In epidemiology, collider stratification bias, the bias resulting from conditioning on a common effect of two causes, is oftentimes considered a type of selection bias, regardless of the conditioning methods employed. In this commentary, we distinguish between two types of collider stratification bias: collider restriction bias due to restricting to one level of a collider (or a descendant of a collider) and collider adjustment bias through inclusion of a collider (or a descendant of a collider) in a regression model. We argue that categorizing collider adjustment bias as a form of selection bias may lead to semantic confusion, as adjustment for a collider in a regression model does not involve selecting a sample for analysis. Instead, we propose that collider adjustment bias can be better viewed as a type of overadjustment bias. We further provide two distinct causal diagram structures to distinguish collider restriction bias and collider adjustment bias. We hope that such a terminological distinction can facilitate easier and clearer communication.

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.

Visualizing External Validity: Graphical Displays to Inform the Extension of Treatment Effects from Trials to Clinical Practice.

BACKGROUND: In the presence of effect measure modification, estimates of treatment effects from randomized controlled trials may not be valid in clinical practice settings. The development and application of quantitative approaches for extending treatment effects from trials to clinical practice settings is an active area of research. METHODS: In this article, we provide researchers with a practical roadmap and four visualizations to assist in variable selection for models to extend treatment effects observed in trials to clinical practice settings and to assess model specification and performance. We apply this roadmap and visualizations to an example extending the effects of adjuvant chemotherapy (5-fluorouracil vs. plus oxaliplatin) for colon cancer from a trial population to a population of individuals treated in community oncology practices in the United States. RESULTS: The first visualization screens for potential effect measure modifiers to include in models extending trial treatment effects to clinical practice populations. The second visualization displays a measure of covariate overlap between the clinical practice populations and the trial population. The third and fourth visualizations highlight considerations for model specification and influential observations. The conceptual roadmap describes how the output from the visualizations helps interrogate the assumptions required to extend treatment effects from trials to target populations. CONCLUSIONS: The roadmap and visualizations can inform practical decisions required for quantitatively extending treatment effects from trials to clinical practice settings.

Optimizing SARS-CoV-2 Pooled Testing Strategies Through Differentiated Pooling for Distinct Groups.

Pooled testing has been successfully used to expand SARS-CoV-2 testing, especially in settings requiring high volumes of screening of lower-risk individuals, but efficiency of pooling declines as prevalence rises. We propose a differentiated pooling strategy that independently optimizes pool sizes for distinct groups with different probabilities of infection to further improve the efficiency of pooled testing. We compared the efficiency (results obtained per test kit used) of the differentiated strategy with a traditional pooling strategy in which all samples are processed using uniform pool sizes under a range of scenarios. For most scenarios, differentiated pooling is more efficient than traditional pooling. In scenarios examined here, an improvement in efficiency of up to 3.94 results per test kit could be obtained through differentiated versus traditional pooling, with more likely scenarios resulting in 0.12 to 0.61 additional results per kit. Under circumstances similar to those observed in a university setting, implementation of our strategy could result in an improvement in efficiency between 0.03 to 3.21 results per test kit. Our results can help identify settings, such as universities and workplaces, where differentiated pooling can conserve critical testing resources.

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.

Predictors of Plasmodium falciparum Infection in the First Trimester Among Nulliparous Women From Kenya, Zambia, and the Democratic Republic of the Congo.

BACKGROUND: Malaria can have deleterious effects early in pregnancy, during placentation. However, malaria testing and treatment are rarely initiated until the second trimester, leaving pregnancies unprotected in the first trimester. To inform potential early intervention approaches, we sought to identify clinical and demographic predictors of first-trimester malaria. METHODS: We prospectively recruited women from sites in the Democratic Republic of the Congo (DRC), Kenya, and Zambia who participated in the ASPIRIN (Aspirin Supplementation for Pregnancy Indicated risk Reduction In Nulliparas) trial. Nulliparous women were tested for first-trimester Plasmodium falciparum infection by quantitative polymerase chain reaction. We evaluated predictors using descriptive statistics. RESULTS: First-trimester malaria prevalence among 1513 nulliparous pregnant women was 6.3% (95% confidence interval [CI], 3.7%-8.8%] in the Zambian site, 37.8% (95% CI, 34.2%-41.5%) in the Kenyan site, and 62.9% (95% CI, 58.6%-67.2%) in the DRC site. First-trimester malaria was associated with shorter height and younger age in Kenyan women in site-stratified analyses, and with lower educational attainment in analyses combining all 3 sites. No other predictors were identified. CONCLUSIONS: First-trimester malaria prevalence varied by study site in sub-Saharan Africa. The absence of consistent predictors suggests that routine parasite screening in early pregnancy may be needed to mitigate first-trimester malaria in high-prevalence settings.

Choice of Outcome in COVID-19 Studies and Implications for Policy: Mortality and Fatality.

In this brief communication, we discuss the confusion of mortality with fatality in the interpretation of evidence in the coronavirus disease 2019 (COVID-19) pandemic, and how this confusion affects the translation of science into policy and practice. We discuss how this confusion has influenced COVID-19 policy in France, Sweden, and the United Kingdom and discuss the implications for decision-making about COVID-19 vaccine distribution. We also discuss how this confusion is an example of a more general statistical fallacy we term the "Missing Link Fallacy."

Associations between HIV, antiretroviral therapy and preterm birth in the US Women's Interagency HIV Study, 1995-2018: a prospective cohort.

OBJECTIVE: To evaluate the associations of HIV infection with preterm birth (PTB), and of HIV antiretroviral therapy (ART) with PTB. METHODS: We analysed singleton live-born pregnancies among women from 1995 to 2019 in the Women's Interagency HIV Study, a prospective cohort of US women with, or at risk for, HIV. The primary exposures were HIV status and ART use before delivery [none, monotherapy or dual therapy, or highly active antiretroviral therapy (HAART)]. The primary outcome was PTB < 34 weeks, and, secondarily, < 28 and < 37 weeks. We analysed self-reported birth data, and separately modelled the associations between HIV and PTB, and between ART and PTB, among women with HIV. We used modified Poisson regression, and adjusted for age, race, parity, tobacco use and delivery year, and, when modelling the impact of ART, duration from HIV diagnosis to delivery, nadir CD4 count, and pre-pregnancy viral load and CD4 count. RESULTS: We analysed 488 singleton deliveries (56% exposed to HIV) to 383 women. The risk of PTB < 34 weeks was similar among women with and without HIV, but the risk of PTB < 37 weeks was higher [32% vs. 23%; adjusted risk ratio (aRR) = 1.43; 95% confidence interval (CI): 1.07-1.91] among women with HIV. The risk of PTB < 34 weeks was lower among women with HIV receiving HAART than among those receiving no ART (7% vs. 26%; aRR:0.19; 95% CI: 0.08-0.44). The associations between HAART and PTB < 28 and < 37 weeks were similar. CONCLUSIONS: Antiretroviral therapy exposure was associated with a decreased risk of PTB among a US cohort of women with HIV. Given the growing concerns about ART and adverse pregnancy outcomes, this finding that ART may be protective for PTB is reassuring.

Hepatitis C coinfection and extrahepatic cancer incidence among people living with HIV.

OBJECTIVES: We assessed the incidence of extrahepatic cancer among people with HIV/HCV coinfection and the potential impact of direct-acting antivirals (DAAs) on extrahepatic cancer risk among people with HIV/HCV coinfection. DESIGN: Our study cohort included adults who initiated HIV care at a CNICS site in the US during 1995-2017, excluding those with previous cancer and without HCV testing. METHODS: We used Cox regression to estimate hazard ratios for extrahepatic cancer incidence among patients with HIV/HCV coinfection compared with those with HIV monoinfection. Standardized morbidity ratio (SMR) weights were used to create a 'pseudopopulation' in which all patients were treated with antiretroviral therapy (ART), and to compare extrahepatic cancer incidence among patients with untreated HIV/HCV coinfection with the incidence that would have been observed if they had been successfully treated for HCV. RESULTS: Of 18 422 adults, 1775 (10%) had HCV RNA and 10 899 (59%) were on ART at baseline. Incidence rates of any extrahepatic cancer among patients with HIV/HCV coinfection and HIV monoinfection were 1027 and 771 per 100 000 person-years, respectively. In SMR-weighted analyses, the risk of any extrahepatic cancer among patients with untreated HCV coinfection at baseline was similar to the risk if they had been successfully treated for HCV. Patients with untreated HCV coinfection at baseline had higher incidence of kidney, lung and inflammation-related cancers than if their HCV had been successfully treated, but these associations were not statistically significant. CONCLUSIONS: We did not find evidence that treating HCV coinfection with DAAs would reduce the incidence of extrahepatic cancers among people with HIV receiving ART.

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.

On the Use of Covariate Supersets for Identification Conditions.

The union of distinct covariate sets, or the superset, is often used in proofs for the identification or the statistical consistency of an estimator when multiple sources of bias are present. However, the use of a superset can obscure important nuances. Here, we provide two illustrative examples: one in the context of missing data on outcomes, and one in which the average causal effect is transported to another target population. As these examples demonstrate, the use of supersets may indicate a parameter is not identifiable when the parameter is indeed identified. Furthermore, a series of exchangeability conditions may lead to successively weaker conditions. Future work on approaches to address multiple biases can avoid these pitfalls by considering the more general case of nonoverlapping covariate sets.

A systematic review of whether COVID-19 randomized controlled trials reported on demographic and clinical characteristics.

PURPOSE: We aim to assess the reporting of key patient-level demographic and clinical characteristics among COVID-19 related randomized controlled trials (RCTs). METHODS: We queried English-language articles from PubMed, Web of Science, clinicaltrials.gov, and the CDC library of gray literature databases using keywords of "coronavirus," "covid," "clinical trial" and "randomized controlled trial" from January 2020 to June 2021. From the search, we conducted an initial review to rule-out duplicate entries, identify those that met inclusion criteria (i.e., had results), and exclude those that did not meet the definition of an RCT. Lastly, we abstracted the demographic and clinical characteristics reported on within each RCT. RESULTS: From the initial 43 627 manuscripts, our final eligible manuscripts consisted of 149 RCTs described in 137 articles. Most of the RCTs (113/149) studied potential treatments, while fewer studied vaccines (29), prophylaxis strategies (5), and interventions to prevent transmission among those infected (2). Study populations ranged from 10 to 38 206 participants (median = 100, IQR: 60-300). All 149 RCTs reported on age, 147 on sex, 50 on race, and 110 on the prevalence of at least one comorbidity. No RCTs reported on income, urban versus rural residence, or other indicators of socioeconomic status (SES). CONCLUSIONS: Limited reporting on race and other markers of SES make it difficult to draw conclusions about specific external target populations without making strong assumptions that treatment effects are homogenous. These findings highlight the need for more robust reporting on the clinical and demographic profiles of patients enrolled in COVID-19 related RCTs.

Projected HIV and Bacterial Sexually Transmitted Infection Incidence Following COVID-19-Related Sexual Distancing and Clinical Service Interruption.

BACKGROUND: The global COVID-19 pandemic has the potential to indirectly impact transmission dynamics and prevention of HIV and other sexually transmitted infections (STI). It is unknown what combined impact reductions in sexual activity and interruptions in HIV/STI services will have on HIV/STI epidemic trajectories. METHODS: We adapted a model of HIV, gonorrhea, and chlamydia for a population of approximately 103 000 men who have sex with men (MSM) in the Atlanta area. Model scenarios varied the timing, overlap, and relative extent of COVID-19-related sexual distancing and service interruption within 4 service categories (HIV screening, preexposure prophylaxis, antiretroviral therapy, and STI treatment). RESULTS: A 50% relative decrease in sexual partnerships and interruption of all clinical services, both lasting 18 months, would generally offset each other for HIV (total 5-year population impact for Atlanta MSM, -227 cases), but have net protective effect for STIs (-23 800 cases). If distancing lasted only 3 months but service interruption lasted 18 months, the total 5-year population impact would be an additional 890 HIV cases and 57 500 STI cases. CONCLUSIONS: Immediate action to limit the impact of service interruptions is needed to address the indirect effects of the global COVID-19 pandemic on the HIV/STI epidemic.

Modeling Combination Interventions to Prevent Human Immunodeficiency Virus in Adolescent Girls and Young Women in South Africa (HIV Prevention Trials Network 068).

BACKGROUND: Combination interventions may be an effective way to prevent human immunodeficiency virus (HIV) in adolescent girls and young women. However, current studies are not designed to understand which specific interventions and combinations will be most effective. We estimate the possible impacts of interventions on a combination of factors associated with HIV. METHODS: We used the g-formula to model interventions on combinations of HIV risk factors to identify those that would prevent the most incident HIV infections, including low school attendance, intimate partner violence, depression, transactional sex, and age-disparate partnerships. We used data from the HIV Prevention Trials Network (HPTN) 068 study in rural South Africa from 2011 to 2017. We estimated HIV incidence under a potential intervention that reduced each risk factor and compared this to HIV incidence under the current distribution of these risk factors. RESULTS: Although many factors had strong associations with HIV, potential intervention estimates did not always suggest large reductions in HIV incidence because the prevalence of risk factors was low. When modeling combination effects, an intervention to increase schooling, decrease depression, and decease transactional sex showed the largest reduction in incident infection (risk difference, -1.4%; 95% confidence interval [CI], -2.7% to -.2%), but an intervention on only transactional sex and depression still reduced HIV incidence by -1.3% (95% CI, -2.6% to -.2%). CONCLUSIONS: To achieve the largest reductions in HIV, both prevalence of the risk factor and strength of association with HIV must be considered. Additionally, intervening on more risk factors may not necessarily result in larger reductions in HIV incidence.

Neurodevelopmental Outcomes of Children Following In Utero Exposure to Zika in Nicaragua.

BACKGROUND: Neurodevelopmental outcomes of asymptomatic children exposed to Zika virus (ZIKV) in utero are not well characterized. METHODS: We prospectively followed 129 newborns without evidence of congenital Zika syndrome (CZS) up to 24 months of age. Participants were classified as ZIKV exposed or ZIKV unexposed. The Mullen Scales of Early Learning (MSEL) was administered in the participants' homes at 6, 12, 15, 18, 21, and 24 months of age by trained psychologists. Sociodemographic data, medical history, and infant anthropometry at birth were collected at each home visit. Our primary outcome was the Mullen Early Learning Composite Score (ECL) at 24 months of age between our 2 exposure groups. Secondary outcomes were differences in MSEL subscales over time and at 24 months. RESULTS: Of 129 infants in whom exposure status could be ascertained, 32 (24.8%) met criteria for in utero ZIKV exposure and 97 (75.2%) did not. There were no differences in maternal age, maternal educational attainment, birthweight, or gestational age at birth between the 2 exposure groups. The adjusted means and standard errors (SEs) for the ELC score between the ZIKV-exposed children compared to ZIKV-unexposed children were 91.4 (SE, 3.1) vs 96.8 (SE, 2.4) at 12 months and 93.3 (SE, 2.9) vs 95.9 (SE, 2.3) at 24 months. In a longitudinal mixed model, infants born to mothers with an incident ZIKV infection (P = .01) and low-birthweight infants (<2500 g) (P = .006) had lower composite ECL scores. CONCLUSIONS: In this prospective cohort of children without CZS, children with in utero ZIKV exposure had lower neurocognitive scores at 24 months.

Clinical Effectiveness of Integrase Strand Transfer Inhibitor-Based Antiretroviral Regimens Among Adults With Human Immunodeficiency Virus: A Collaboration of Cohort Studies in the United States and Canada.

BACKGROUND: Integrase strand transfer inhibitor (InSTI)-based regimens are now recommended as first-line antiretroviral therapy (ART) for adults with human immunodeficiency virus, but evidence on long-term clinical effectiveness of InSTI-based regimens remains limited. We examined whether InSTI-based regimens improved longer-term clinical outcomes. METHODS: We included participants from clinical cohorts in the North American AIDS Cohort Collaboration on Research and Design who initiated their first ART regimen, containing either InSTI (ie, raltegravir, dolutegravir, and elvitegravir-cobicistat) or efavirenz (EFV) as an active comparator, between 2009 and 2016. We estimated observational analogs of 6-year intention-to-treat and per-protocol risks, risk differences (RDs), and hazard ratios (HRs) for the composite outcome of AIDS, acute myocardial infarction, stroke, end-stage renal disease, end-stage liver disease, or death. RESULTS: Of 15 993 participants, 5824 (36%) initiated an InSTI-based and 10 169 (64%) initiated an EFV-based regimen. During the 6-year follow-up, 440 in the InSTI group and 1097 in the EFV group incurred the composite outcome. The estimated 6-year intention-to-treat risks were 14.6% and 14.3% for the InSTI and EFV groups, respectively, corresponding to a RD of 0.3% (95% confidence interval, -2.7% to 3.3%) and a HR of 1.08 (.97-1.19); the estimated 6-year per-protocol risks were 12.2% for the InSTI group and 11.9% for the EFV group, corresponding to a RD of 0.3% (-3.0% to 3.7%) and a HR of 1.09 (.96-1.25). CONCLUSIONS: InSTI- and EFV-based initial ART regimens had similar 6-year composite clinical outcomes. The risk of adverse clinical outcomes remains substantial even when initiating modern ART.

Introducing longitudinal cumulative dose to describe chemotherapy patterns over time: Case study of a colon cancer trial.

Adjuvant chemotherapy regimens take months to complete. Despite this, studies evaluate chemotherapy adherence via measures assessed at the end of treatment (eg, number of patients missing any dose, relative dose intensity [RDI]). This approach ignores information like the timing of treatment delays. We propose longitudinal cumulative dose (LCD) to integrate impacts of dose reductions, missed doses and dose delays over time. We obtained data from the 2246 participants in the MOSAIC trial randomized to FOLFOX (all three agents) or 5-FU/LV (only 5-fluorouracil and leucovorin). We evaluated proportions of patients stopping treatment early and reducing, missing or delaying a dose in each arm for each chemotherapy agent at each cycle. We calculated LCD, the fraction of the final standard dose a participant reached by a given day, for each participant and each agent and compared it over time and at 24 weeks between treatment arms. Participants randomized to FOLFOX were more likely to stop treatment, reduce doses, miss doses or delay cycles; these differences increased over time. Median LCD for oxaliplatin in the FOLFOX arm at 24 weeks was 77%. The LCD for 5-fluorouracil differed between arms (FOLFOX arm median: 81%; 5-FU/LV arm median: 96%). Visualizing LCD highlighted the timing of deviations from standard administration in a way RDI could not, with major differences in 5-fluorouracil LCD across treatment arms beginning after the sixth dose. Further evaluation of LCD and its impacts on clinical outcomes may clarify mechanisms for heterogeneous patient outcomes.

Transporting Subgroup Analyses of Randomized Controlled Trials for Planning Implementation of New Interventions.

Subgroup analyses of randomized controlled trials guide resource allocation and implementation of new interventions by identifying groups of individuals who are likely to benefit most from the intervention. Unfortunately, trial populations are rarely representative of the target populations of public health or clinical interest. Unless the relevant differences between trial and target populations are accounted for, subgroup results from trials might not reflect which groups in the target population will benefit most from the intervention. Transportability provides a rigorous framework for applying results derived in potentially highly selected study populations to external target populations. The method requires that researchers measure and adjust for all variables that 1) modify the effect of interest and 2) differ between the target and trial populations. To date, applications of transportability have focused on the external validity of overall study results and understanding within-trial heterogeneity; however, this approach has not yet been used for subgroup analyses of trials. Through an example from the Iniciativa Profilaxis Pre-Exposición (iPrEx) study (multiple countries, 2007-2010) of preexposure prophylaxis for human immunodeficiency virus, we illustrate how transporting subgroup analyses can produce target-specific subgroup effect estimates and numbers needed to treat. This approach could lead to more tailored and accurate guidance for resource allocation and cost-effectiveness analyses.

Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!).

Machine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspecification. Using a question-and-answer format, we provide an introduction and orientation for epidemiologists interested in using machine learning but concerned about potential bias or loss of rigor due to use of "black box" models. We conclude with sample software code that may lower the barrier to entry to using these techniques.