Fang Shao

Background/Objectives: Pharmacovigilance studies typically evaluate adverse events as isolated drug–event pairs, potentially overlooking symptoms and functional impairments reported concurrently within the same case. We investigated co-reported adverse event patterns for tofacitinib and upadacitinib in patients with rheumatoid arthritis (RA). Methods: FAERS reports submitted between 2004 and 2025 were included if tofacitinib or upadacitinib was recorded as the primary suspect drug for RA. Disproportionality analysis was integrated with three co-occurrence network approaches, Bayesian network analysis, and time-to-onset (TTO) analysis. Methotrexate served as an indication-matched comparator. Network analyses were stratified by reporter and submission type, and TTO was reassessed for the common 2019–2025 period. FAERS signals were also compared descriptively with Canada Vigilance data. Results: We identified 57,438 tofacitinib-related and 29,869 upadacitinib-related reports, yielding 316 and 413 positive Preferred Term signals, respectively. In the tofacitinib networks, fatigue was consistently associated with pain, mood-related terms, treatment experience, and gastrointestinal and infectious events. Upadacitinib reports exhibited a distinct pattern, with falls, pain, functional limitations, and injuries occupying central positions. Reduced grip strength, decreased joint range of motion, and loss of personal independence formed a recurrent functional cluster. Methotrexate networks were primarily organized around disease-activity terms. The median reported TTO was 34 days for tofacitinib and 115 days for upadacitinib; corresponding values for the 2019–2025 period were 59 and 115 days, respectively. These intervals should not be interpreted as true onset latency or as established hazard patterns. Conclusions: Co-reporting analysis yielded insights beyond individual disproportionality signals. Tofacitinib reports were predominantly characterized by fatigue and associated symptoms, whereas upadacitinib reports more prominently featured functional limitations, falls, and injuries.

Background: mRNA vaccines, first approved in December 2020, have been used globally to prevent infectious diseases, and those for treating cancers are being developed. Safety-related labelling changes of Comirnaty and Spikevax were made in June 2025; however, concerns remain. This study assessed the potential risks associated with mRNA vaccines on the indications previously approved, utilizing Real-World Data (RWD) of Adverse Events Following Immunization (AEFIs) derived from the Vaccine Adverse Event Reporting System (VAERS) and Academic Literature Databases (ALD). Methods: A Disproportionality Analysis (DPA) was performed using the Reporting Odds Ratio (ROR) and the Bayesian Confidence Propagation Neural Network (BCPNN) algorithm on spontaneous case reports from VAERS. Statistical positive signals were cross-validated with literature case reports from ALD to provide more comprehensive medical descriptions and clearer causal assessments, and compared with safety information documented in clinical trials and on vaccine labelling. Time-to-onset, stratified, and immunization schedule analyses were conducted to characterize the safety profiles of mRNA vaccines. Results: In total, 5,040,725 spontaneous case reports and 4,387 literature case reports were analyzed. In both VAERS and ALD, new signals involving blood and lymphatic system disorders (e.g., thrombotic thrombocytopenic purpura) and ear and labyrinth disorders (e.g., deafness) were detected from Comirnaty as Designated Medical Events (DMEs), while blood and lymphatic system disorders (e.g., thrombotic thrombocytopenic purpura) from Spikevax in ALD only. No new signals were detected from other vaccines on the DMEs list. In VAERS, Serious Adverse Events (SAEs) were more common in females, while death risk was higher in males. In ALD, SAEs were more common in males for most mRNA vaccines, except Comirnaty. Medical history emerged as a key risk factor for SAEs, particularly among older adults. Conclusions: Statistically significant safety signals were detected across all mRNA vaccines based on five-year cumulative RWD, indicating the need of intensified monitoring of specific populations, including older adults and individuals with medical histories, alongside further optimization of vaccination strategies.

Background: Time to benefit (TTB) has emerged as a clinically interpretable estimand for characterizing when treatment effects become meaningful over time. Unlike conventional survival summaries, TTB is implicitly defined through marginal differences in survival probabilities and is therefore highly sensitive to modeling assumptions. In multicenter studies involving clustered time-to-event data, unobserved heterogeneity and misspecification of the baseline hazard present additional challenges for coherent TTB estimation. Methods: We propose a unified framework for estimating TTB in clustered survival settings using marginal survival modeling with shared frailty. Specifically, TTB is defined on the marginal population scale by integrating over the frailty distribution, ensuring coherence between the estimand and its clinical interpretation. Both parametric and flexible spline-based baseline hazard models are evaluated. Uncertainty is quantified using the Delta method and Monte Carlo (MC)-based inference procedures. Extensive simulation studies are conducted to characterize estimator behavior under varying degrees of heterogeneity, censoring, and hazard misspecification. Furthermore, the framework is illustrated using data from the Systolic Blood Pressure Intervention Trial (SPRINT), a large multicenter randomized clinical trial. Results: Simulation results indicate that ignoring latent heterogeneity or misspecifying the baseline hazard can bias TTB estimation and produce miscalibrated confidence intervals, particularly under small absolute risk reduction thresholds. Flexible hazard models combined with MC-based inference yield more stable estimates and improved coverage in the presence of model misspecification. In the SPRINT application, TTB point estimates remained relatively consistent across modeling approaches, while statistically significant frailty effects revealed meaningful between-site heterogeneity, highlighting its importance for accurate uncertainty quantification. Conclusions: TTB is a model-sensitive implicit estimand; reliable estimation in clustered survival settings requires explicit alignment among the estimand definition, the survival model, and the inference strategy. The proposed framework provides a principled and practical approach to TTB estimation in multicenter studies, facilitating transparent and interpretable reporting of TTB in both clinical and real-world research contexts.

Cluster randomized trials (CRTs) require balanced baseline covariates to yield unbiased estimates of treatment effects. Existing approaches such as constra

In nutrition research, confounding is an important challenge. Owing to the complexities involved in dietary interventions, such research is often subject to three types of confounders: measured confounders, unmeasured confounders, and time-varying confounders. Inadequate control of these confounders can lead to distorted research conclusions and even undermine the credibility of dietary guidelines. This review systematically summarizes strategies for confounder control in nutrition research, including confounder identification at the pre-study stage, confounder control at the design and analysis stages, and relevant R packages for practical implementation. In addition, the review collates and compares the specific requirements for confounding control in general reporting guidelines and their nutrition-specific extensions, thereby providing reporting guidance for the identification and control of confounders in nutrition research.

Time to Benefit (TTB) is a critical metric in clinical practice, reflecting the duration required to achieve therapeutic goals post-treatment. Traditionally, TTB estimation has relied on Bayesian Weibull regression, which, despite its merits, can be computationally intensive. To address this, we propose and evaluate Frequentist methods as efficient alternatives to approximate Bayesian TTB estimation. We evaluated three Frequentist methods, parametric delta, Monte Carlo, and nonparametric bootstrap, for TTB estimation, comparing their performance with the Bayesian approach. Extensive simulations demonstrated that the proposed Frequentist methods outperformed the Bayesian method in efficiency. Real-world data applications further validated these findings, with the Monte Carlo (MC) method exhibiting significantly faster computational speed compared to the nonparametric bootstrap, while the Bayesian method was the least efficient. The proposed Frequentist methods offer significant advantages to approximate the Bayesian approach for TTB estimation, particularly in efficiency and practicality. The Monte Carlo method, with its median point estimate and percentile confidence intervals, is the recommended choice for its balance of efficacy and expedience.

Previous studies have suggested a potential connection between schizophrenia (SCZ) and sleep and eating disorders. However, these studies have not sufficiently accounted for potential confounding factors, leaving the true causal relationship unclear. Understanding this causal link is essential for developing effective prevention and treatment strategies. To address this gap, we aim to investigate the causal effect of SCZ on sleep and eating disorders using Mendelian randomization (MR) analysis. This method offers a more robust approach by leveraging genetic variants as instrumental variables to rigorously examine the relationship between SCZ and its comorbidities. We conducted bidirectional MR analyses using genome-wide association study summary statistics of SCZ, sleep disorders, and eating disorders. These analyses were conducted after confirming adherence to the 3 core MR assumptions, removing instrumental variables with confounding effects, and directionally harmonizing all data. Then we used Cochran Q test, MR-Egger intercept analysis, and leave-one-out method to conduct the sensitivity analysis of this study to test its heterogeneity and pleiotropy. The results of the inverse-variance weighted (IVW) approach suggest that SCZ increases the risk of sleep disorders (IVW: odds ratio [OR] = 1.041, 95% confidence interval [CI]: 1.012–1.070, P < .01), whereas studies in the opposite direction have not found an effect of sleep disorders on SCZ. The results of IVW suggest that SCZ increases the risk of eating disorders (IVW: OR = 1.228, 95% CI: 1.090–1.384, P < .001), and the weighted median (WM) method similarly provided evidence that SCZ increases the risk of eating disorders (WM: OR = 1.200, 95% CI: 1.019–1.407, P < .05). This study concluded that SCZ is causally associated with sleep and eating disorders. In clinical practice, psychiatrists should pay attention to the daily sleep and eating status of patients with SCZ, and take appropriate measures and countermeasures as early as possible if there is any abnormality.

Lung and gastrointestinal diseases often occur together, leading to more adverse health outcomes than when a disease of one of these systems occurs alone. However, the potential genetic mechanisms underlying lung-gastrointestinal comorbidities remain unclear. Here, we leverage lung and gastrointestinal trait data from individuals of European, East Asian and African ancestries, to perform a large-scale genetic cross trait analysis, followed by functional annotation and Mendelian randomization analysis to explore the genetic mechanisms involved in the development of lung-gastrointestinal comorbidities. Notably, we find significant genetic correlations between 27 trait pairs among the European population. The highest correlation is between chronic bronchitis and peptic ulcer disease. At the variant level, we identify 42 candidate pleiotropic genetic variants (3 of them previously uncharacterized) in 14 trait pairs by integrating cross-trait meta-analysis, fine-mapping and colocalization analyses. We also find 66 candidate pleiotropic genes, most of which were enriched in immune or inflammatory response-related activities. Causal inference approaches result in 4 potential lung-gastrointestinal associations. Introducing the gut microbiota as a variable establishes a relationship between the genus Parasutterella, gastro-oesophageal reflux disease and asthma. In summary, our findings highlight the genetic relationship between lung and gastrointestinal diseases, providing insights into the genetic mechanisms underlying the development of lung gastrointestinal comorbidities.