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Background Survival and health status (eg, symptoms and quality of life) are key outcomes in clinical trials of heart failure treatment. However, health status can only be recorded on survivors, potentially biasing treatment effect estimates when there is differential survival across treatment groups. Joint modeling of survival and health status can address this bias. Methods and Results We analyzed patient-level data from the PARTNER 1B trial (Placement of Aortic Transcatheter Valves) of transcatheter aortic valve replacement versus standard care. Health status was quantified with the Kansas City Cardiomyopathy Questionnaire (KCCQ) at randomization, 1, 6, and 12 months. We compared hazard ratios for survival and mean differences in KCCQ scores at 12 months using several models: the original growth curve model for KCCQ scores (ignoring death), separate Bayesian models for survival and KCCQ scores, and a Bayesian joint longitudinal-survival model fit to either 12 or 30 months of survival follow-up. The benefit of transcatheter aortic valve replacement on 12-month KCCQ scores was greatest in the joint-model fit to all survival data (mean difference, 33.7 points; 95% credible intervals [CrI], 24.2-42.4), followed by the joint-model fit to 12 months of survival follow-up (32.3 points; 95% CrI, 22.5-41.5), a Bayesian model without integrating death (30.4 points; 95% CrI, 21.4-39.3), and the original growth curve model (26.0 points; 95% CI, 18.7-33.3). At 12 months, the survival benefit of transcatheter aortic valve replacement was also greater in the joint model (hazard ratio, 0.50; 95% CrI, 0.32-0.73) than in the nonjoint Bayesian model (0.54; 95% CrI, 0.37-0.75) or the original Kaplan-Meier estimate (0.55; 95% CI, 0.40-0.74). Conclusions In patients with severe symptomatic aortic stenosis and prohibitive surgical risk, the estimated benefits of transcatheter aortic valve replacement on survival and health status compared with standard care were greater in joint Bayesian models than other approaches.

Objective: This proposed scoping review aims to examine studies assessing the impact of drug shortages on population-level drug utilization trends. The objectives of this review are to a) assess which drugs have been studied and describe associated drug characteristics, b) determine jurisdictions and healthcare settings that have conducted these studies, and c) describe how changes in drug use and the extent of shortage impacts are reported in literature. Introduction: Drug shortages continue to impair drug access and delivery of quality care across the world. However, the impact of drug supply disruptions on availability and drug use are understudied in current literature. This proposed scoping review will identify this gap and inform future research initiatives aimed at determining the real-world impacts of drug shortages. Inclusion criteria: Published and unpublished observational studies reporting on the effects of drug supply chain disruptions (shortages, discontinuations, and safety-based withdrawals) on consequent utilization trends faced by pharmaceutical products (i.e. prescription drugs, over-the-counter drugs, vaccines, therapy products, pharmaceutical solutions). Literature reviews, meta-analyses, randomized control trials, case series, case reports, and opinion pieces will be excluded. Methods: The search strategy will combine two key search concepts: drug shortages and drug utilization. The search will be conducted in MEDLINE and EMBASE. This will be followed by an extensive grey literature search in grey literature databases, targeted websites and Google. Furthermore, reference lists of included articles will be searched. Articles will be independently screened, selected and extracted by two reviewers. Data will be descriptively analyzed and presented in tables. Trial registration: Review registration number: Open Science Framework, https://osf.io/2p6e5. [ABSTRACT FROM AUTHOR]

A Real-World Evidence (RWE) scientific working group of the American Statistical Association Biopharmaceutical Section endeavors to elevate statistical practices of generating real-world evidence to support regulatory decision making. Randomized clinical trials have been the gold standard for evaluating the safety and efficacy of medical products. However, in many cases, their costs, duration, limited generalizability, and ethical or technical infeasibility might have motivated some to consider alternatives, such as real-world studies. Yet, the studies conducted in a real-world setting may be susceptible to the lack of randomization and the presence of confounding bias. To generate robust real-world evidence (RWE) from analyzing real-world data (RWD), we have proposed an RWE Causal Inference Roadmap with targeted learning. Three key steps in the roadmap are (1) define a target estimand that aligns with the causal estimand of the study objective; (2) select an efficient estimator for estimating the target estimand and an estimator of its uncertainty; and (3) evaluate the robustness of conclusions to violations of untestable causal assumptions. In this article, we demonstrate how to apply the roadmap to generate RWE using synthetic data based on a real oncology trial. [ABSTRACT FROM AUTHOR]

Concurrent and longitudinal relations between the amount of romantic experience and psychosocial adjustment were examined in a 1-year study of a community based sample of 200 tenth graders. Adolescents, parents, and friends completed measures of psychosocial adjustment. The amount of romantic experience was associated with higher reports of social acceptance, friendship competence, and romantic competence; at the same time, romantic experience also was associated with greater substance use, more delinquent behavior, and more frequent genital sexual behavior. The amount of romantic experience predicted increased substance use and genital sexual behavior over a 1-year period, whereas social acceptance predicted increased romantic experience. These relations with romantic experience were significant even when genital sexual behavior and social acceptance were taken into account, underscoring the unique contribution of romantic experiences. (Contains 1 table and 7 figures.)

Although success in managing evolving peer relationships is linked to critical adolescent outcomes, little is known about the specific factors that lead to success or failure in peer relationship development across adolescence. This longitudinal study examines the role of adolescents' level of ego development as a predictor of the future course of several facets of friendship development in early adolescence. Ego development was assessed in a community sample of adolescents at age 13. Several facets of adolescent friendship were also assessed at 13 and then reassessed 1 year later, including adolescent intimate behavior during a supportive interaction with their best friends, adolescent reports of psychological security in their friendships, and peer-rated popularity. As predicted, ego development not only explained concurrent levels of peer functioning but also predicted markers of change over time in each of the assessed domains of peer functioning. Implications for ego development in increasing our understanding of individual differences in adolescent friendship development are discussed. (Contains 3 tables.)

Background: Hydroxychloroquine and ivermectin received widespread attention after initial studies suggested that they were effective against COVID-19. However, several of these studies were later discredited.Objectives: We explored the impact of scientific articles, public announcements and social media posts on hydroxychloroquine and ivermectin purchases in the USA and Canada during the COVID-19 pandemic.Methods: We conducted a retrospective, population-based time series analysis of retail hydroxychloroquine and ivermectin purchases in the USA and Canada from February 2016 through to December 2021, using IQVIA's Multinational Integrated Data Analysis database. We fitted the purchasing rates with interventional autoregressive integrated moving average models. We used Google Trends to identify the most influential interventions to include in the models.Results: There were significant pulse increases in hydroxychloroquine purchases in March 2020 in both the USA (P < 0.0001) and Canada (P < 0.0001). For ivermectin, there were no significant changes in April 2020 in either the USA (P = 0.41) or Canada (P = 0.16); however, significant pulse increases occurred from December 2020 to January 2021 in both the USA (P = 0.0006) and Canada (P < 0.0001), as well as significant ramp increases from April to August 2021 in both the USA (P < 0.0001) and Canada (P = 0.02). The increases in ivermectin purchases were larger in the USA than in Canada.Conclusions: Increases in hydroxychloroquine and ivermectin purchasing rates aligned with controversial scientific articles and social media posts. This highlights the importance of scientific integrity and disseminating accurate epidemiologic information during pandemics. [ABSTRACT FROM AUTHOR]

Real-world data (RWD), such as electronic health records, reimbursement requests as adjudicated by health insurance companies, and health survey data as collected by government agencies or other research organizations, are increasingly used in drug development. Regulatory agencies, public-private partnerships, and professional organizations have initiated major programs and released guidance or guidelines to address challenges in the use of real-world evidence (RWE) to inform regulatory decision making. Since statistical research and considerations in the design, analysis, and interpretation of RWE studies is still a nascent area, the ASA Biopharmaceutical Section established a scientific working group to survey the existing practices and identify opportunities in the area. This article reviews the current landscape of using external data such as RWD/RWE to inform clinical study design and analysis. Two companion articles review the landscape of using RWE in medical product label expansion and of causal inference frameworks to generate RWE. [ABSTRACT FROM AUTHOR]

As real-world data (RWD) become more readily available, the regulatory agencies, medical product developers, and other key stakeholders have increasing interests in exploring the use of real-world evidence (RWE) to support regulatory decisions alternative to traditional clinical trials. To facilitate and promote statistical research in design, analysis, and interpretation of RWE studies for regulatory decision making, the ASA Biopharmaceutical Section established the RWE Scientific Working Group to address challenges and identify opportunities in the statistical research of this area. This article provides a landscape assessment of relevant causal inference frameworks for study design and analysis that generates RWE. Two companion articles of the Working group review statistical landscape on the use of RWE for medical product label expansion and the other on using RWE to inform clinical trial design and analysis. [ABSTRACT FROM AUTHOR]