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Policymakers use results from randomized controlled trials to inform decisions about whether to implement treatments in target populations. Various methods - including inverse probability weighting, outcome modeling, and Targeted Maximum Likelihood Estimation - that use baseline data available in both the trial and target population have been proposed to generalize the trial treatment effect estimate to the target population. Often the target population is significantly larger than the trial sample, which can cause estimation challenges. We conduct simulations to compare the performance of these methods in this setting. We vary the size of the target population, the proportion of the target population selected into the trial, and the complexity of the true selection and outcome models. All methods performed poorly when the trial size was only 2% of the target population size or the target population included only 1,000 units. When the target population or the proportion of units selected into the trial was larger, some methods, such as outcome modeling using Bayesian Additive Regression Trees, performed well. We caution against generalizing using these existing approaches when the target population is much larger than the trial sample and advocate future research strives to improve methods for generalizing to large target populations.

Generalizability methods are increasingly used to make inferences about the effect of interventions in target populations using a study sample. Most existing methods to generalize effects from sample to population rely on the assumption that subgroup-specific effects generalize directly. However, researchers may be concerned that in fact subgroup-specific effects differ between sample and population. In this brief report, we explore the generalizability of subgroup effects. First, we derive the bias in the sample average treatment effect estimator as an estimate of the population average treatment effect when subgroup effects in the sample do not directly generalize. Next, we present a Monte Carlo simulation to explore bias due to unmeasured heterogeneity of subgroup effects across sample and population. Finally, we examine the potential for bias in an illustrative data example. Understanding the generalizability of subgroup effects may lead to increased use of these methods for making externally valid inferences of treatment effects using a study sample.

There is an urgent need to measure the impacts of COVID-19 among gay men and other men who have sex with men (MSM). We conducted a cross-sectional survey with a global sample of gay men and other MSM (n = 2732) from April 16, 2020 to May 4, 2020, through a social networking app. We characterized the economic, mental health, HIV prevention and HIV treatment impacts of COVID-19 and the COVID-19 response, and examined whether sub-groups of our study population are disproportionately impacted by COVID-19. Many gay men and other MSM not only reported economic and mental health consequences, but also interruptions to HIV prevention and testing, and HIV care and treatment services. These consequences were significantly greater among people living with HIV, racial/ethnic minorities, immigrants, sex workers, and socio-economically disadvantaged groups. These findings highlight the urgent need to mitigate the negative impacts of COVID-19 among gay men and other MSM.

Randomized trials play an important role in estimating the effect of a policy or social work program in a given population. While most trial designs benefit from strong internal validity, they often lack external validity, or generalizability, to the target population of interest. In other words, one can obtain an unbiased estimate of the study sample average treatment effect from a randomized trial; however, this estimate may not equal the target population average treatment effect if the study sample is not fully representative of the target population. This article provides an overview of existing strategies to assess and improve upon the generalizability of randomized trials, both through statistical methods and study design, as well as recommendations on how to implement these ideas in social work research. [Paper presented at the Stockholm Conference on Behavioral and Social Intervention Research, 11-12 May, 2017.]

Introduction: While randomized controlled trials remain the reference standard for evaluating treatment efficacy, there is an increased interest in the use of external control arms (ECA), namely in oncology, using real-world data (RWD). Challenges related to measurement of real-world oncology endpoints, like progression-free survival (PFS), are one factor limiting the use and acceptance of ECAs as comparators to trial populations. Differences in how and when disease assessments occur in the real-world may introduce measurement error and limit the comparability of real-world PFS (rwPFS) to trial progression-free survival. While measurement error is a known challenge when conducting an externally-controlled trial with real-world data, there is limited literature describing key contributing factors, particularly in the context of multiple myeloma (MM). Methods: We distinguish between biases attributed to how endpoints are derived or ascertained (misclassification bias) and when outcomes are observed or assessed (surveillance bias). We further describe how misclassification of progression events (i.e., false positives, false negatives) and irregular assessment frequencies in multiple myeloma RWD can contribute to these biases, respectively. We conduct a simulation study to illustrate how these biases may behave, both individually and together. Results: We observe in simulation that certain types of measurement error may have more substantial impacts on comparability between mismeasured median PFS (mPFS) and true mPFS than others. For instance, when the observed progression events are misclassified as either false positives or false negatives, mismeasured mPFS may be biased towards earlier (mPFS bias = −6.4 months) or later times (mPFS bias = 13 months), respectively. However, when events are correctly classified but assessment frequencies are irregular, mismeasured mPFS is more similar to the true mPFS (mPFS bias = 0.67 months). Discussion: When misclassified progression events and irregular assessment times occur simultaneously, they may generate bias that is greater than the sum of their parts. Improved understanding of endpoint measurement error and how resulting biases manifest in RWD is important to the robust construction of ECAs in oncology and beyond. Simulations that quantify the impact of measurement error can help when planning for ECA studies and can contextualize results in the presence of endpoint measurement differences. [ABSTRACT FROM AUTHOR]

Introduction Sub-Saharan Africa bears more than two-thirds of the worldwide burden of HIV; however, data among transgender women from the region are sparse. Transgender women across the world face significant vulnerability to HIV. This analysis aimed to assess HIV prevalence as well as psychosocial and behavioral drivers of HIV infection among transgender women compared with cisgender (non-transgender) men who have sex with men (cis-MSM) in 8 sub-Saharan African countries. Methods and findings Respondent-driven sampling targeted cis-MSM for enrollment. Data collection took place at 14 sites across 8 countries: Burkina Faso (January-August 2013), Côte d'Ivoire (March 2015-February 2016), The Gambia (July-December 2011), Lesotho (February-September 2014), Malawi (July 2011-March 2012), Senegal (February-November 2015), Swaziland (August-December 2011), and Togo (January-June 2013). Surveys gathered information on sexual orientation, gender identity, stigma, mental health, sexual behavior, and HIV testing. Rapid tests for HIV were conducted. Data were merged, and mixed effects logistic regression models were used to estimate relationships between gender identity and HIV infection. Among 4,586 participants assigned male sex at birth, 937 (20%) identified as transgender or female, and 3,649 were cis-MSM. The mean age of study participants was approximately 24 years, with no difference between transgender participants and cis-MSM. Compared to cis-MSM participants, transgender women were more likely to experience family exclusion (odds ratio [OR] 1.75, 95% CI 1.42-2.16, p < 0.001), rape (OR 1.95, 95% CI 1.63-2.36, p < 0.001), and depressive symptoms (OR 1.30, 95% CI 1.12-1.52, p < 0.001). Transgender women were more likely to report condomless receptive anal sex in the prior 12 months (OR 2.44, 95% CI 2.05-2.90, p < 0.001) and to be currently living with HIV (OR 1.81, 95% CI 1.49-2.19, p < 0.001). Overall HIV prevalence was 25% (235/926) in transgender women and 14% (505/3,594) in cis-MSM. When adjusted for age, condomless receptive anal sex, depression, interpersonal stigma, law enforcement stigma, and violence, and the interaction of gender with condomless receptive anal sex, the odds of HIV infection for transgender women were 2.2 times greater than the odds for cis-MSM (95% CI 1.65-2.87, p < 0.001). Limitations of the study included sampling strategies tailored for cis-MSM and merging of datasets with non-identical survey instruments. Conclusions In this study in sub-Saharan Africa, we found that HIV burden and stigma differed between transgender women and cis-MSM, indicating a need to address gender diversity within HIV research and programs., Author(s): Tonia Poteat 1,*, Benjamin Ackerman 2, Daouda Diouf 3, Nuha Ceesay 4, Tampose Mothopeng 5, Ky-Zerbo Odette 6, Seni Kouanda 7,8, Henri Gautier Ouedraogo 7,8, Anato Simplice 9, Abo [...]

Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform policy and programming efforts, yet their results may not generalize well to a relevant target population due to potential differences in effect moderators between the trial and population. Statistical methods have been developed to improve generalizability by combining trials and population data, and weighting the trial to resemble the population on baseline covariates. Large‐scale surveys in fields such as health and education with complex survey designs are a logical source for population data; however, there is currently no best practice for incorporating survey weights when generalizing trial findings to a complex survey. We propose and investigate ways to incorporate survey weights in this context. We examine the performance of our proposed estimator through simulations in comparison to estimators that ignore the complex survey design. We then apply the methods to generalize findings from two trials—a lifestyle intervention for blood pressure reduction and a web‐based intervention to treat substance use disorders—to their respective target populations using population data from complex surveys. The work highlights the importance in properly accounting for the complex survey design when generalizing trial findings to a population represented by a complex survey sample. [ABSTRACT FROM AUTHOR]

Background: Restricted and Repetitive Behaviors (RRB), one of the core symptom categories for Autism Spectrum Disorders (ASD), comprises heterogeneous groups of behaviors. Previous research indicates that there are two or more factors (subcategories) within the RRB domain. In an effort to identify common variants associated with RRB, we have carried out a genome-wide association study (GWAS) using the Autism Genetic Resource Exchange (AGRE) dataset (n = 1,335, all ASD probands of European ancestry) for each identified RRB subcategory, while allowing for comparisons of associated single nucleotide polymorphisms (SNPs) with associated SNPs in the same set of probands analyzed using all the RRB subcategories as phenotypes in a multivariate linear mixed model. The top ranked SNPs were then explored in an independent dataset. Results: Using principal component analysis of item scores obtained from Autism Diagnostic Interview-Revised (ADI-R), two distinct subcategories within Restricted and Repetitive Behaviors were identified: Repetitive Sensory Motor (RSM) and Insistence on Sameness (IS). Quantitative RSM and IS scores were subsequently used as phenotypes in a GWAS using the AGRE ASD cohort. Although no associated SNPs with genome-wide significance (P < 5.0E-08) were detected when RSM or IS were analyzed independently, three SNPs approached genome-wide significance when RSM and IS were considered together using multivariate association analysis. These included the top IS-associated SNP, rs62503729 (P-value = 6.48E-08), which is located within chromosome 8p21.2-8p21.1, a locus previously linked to schizophrenia. Notably, all of the most significantly associated SNPs are located in close proximity to STMN4 and PTK2B, genes previously shown to function in neuron development. In addition, several of the top-ranked SNPs showed correlations with STMN4 mRNA expression in adult CEU (Caucasian and European descent) human prefrontal cortex. However, the association signals within chromosome 8p21.2-8p21.1 failed to replicate in an independent sample of 2,588 ASD probands; the insufficient sample size and between-study heterogeneity are possible explanations for the non-replication. Conclusions: Our analysis indicates that RRB in ASD can be represented by two distinct subcategories: RSM and IS. Subsequent univariate and multivariate genome-wide association studies of these RRB subcategories enabled the detection of associated SNPs at 8p21.2-8p21.1. Although these results did not replicate in an independent ASD dataset, genomic features of this region and pathway analysis suggest that common variants in 8p21.2-8p21.1 may contribute to RRB, particularly IS. Together, these observations warrant future studies to elucidate the possible contributions of common variants in 8p21.2-8p21.1 to the etiology of RSM and IS in ASD. [ABSTRACT FROM AUTHOR]

Background: The coronavirus pandemic has necessitated a range of population-based measures to stem the spread of infection. These measures may be associated with disruptions to other health services including for gay, bisexual, and other men who have sex with men (MSM) at risk for or living with HIV. Here, we assess the relationship between stringency of COVID-19 control measures and interruptions to HIV prevention and treatment services for MSM., Setting: Data for this study were collected between April 16, 2020, and May 24, 2020, as part of a COVID-19 Disparities Survey implemented by the gay social networking app, Hornet. Pandemic control measures were quantified using the Oxford Government Response Tracker Stringency Index: each country received a score (0-100) based on the number and strictness of 9 indicators related to restrictions, closures, and travel bans., Methods: We used a multilevel mixed-effects generalized linear model with Poisson distribution to assess the association between stringency of pandemic control measures and access to HIV services., Results: A total of 10,654 MSM across 20 countries were included. Thirty-eight percent (3992/10,396) reported perceived interruptions to in-person testing, 55% (5178/9335) interruptions to HIV self-testing, 56% (5171/9173) interruptions to pre-exposure prophylaxis, and 10% (990/9542) interruptions to condom access. For every 10-point increase in stringency, there was a 3% reduction in the prevalence of perceived access to in-person testing (aPR: 0·97, 95% CI: [0·96 to 0·98]), a 6% reduction in access to self-testing (aPR: 0·94, 95% CI: [0·93 to 0·95]), and a 5% reduction in access to pre-exposure prophylaxis (aPR: 0·95, 95% CI: [0·95 to 0·97]). Among those living with HIV, 20% (218/1105) were unable to access their provider; 65% (820/1254) reported being unable to refill their treatment prescription remotely., Conclusions: More stringent responses were associated with decreased perceived access to services. These results support the need for increasing emphasis on innovative strategies in HIV-related diagnostic, prevention, and treatment services to minimize service interruptions during this and potential future waves of COVID-19 for gay men and other MSM at risk for HIV acquisition and transmission., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)