Saptarshi Chakraborty

Spontaneous reporting system databases are key resources for post‐marketing surveillance, providing real‐world evidence (RWE) on the adverse events (AEs) of regulated drugs or other medical products. Various statistical methods have been proposed for AE signal detection in these databases, flagging drug‐specific AEs with disproportionately high observed counts compared to expected counts under independence. However, signal detection remains challenging for rare AEs or newer drugs, which receive small observed and expected counts and thus suffer from reduced statistical power. Principled information sharing on signal strengths across drugs/AEs is crucial in such cases to enhance signal detection. However, existing methods typically ignore complex between‐drug associations on AE signal strengths, limiting their ability to detect signals. We propose novel local–global mixture Dirichlet process (DP) prior‐based nonparametric Bayesian models to capture these associations, enabling principled information sharing between drugs while balancing flexibility and shrinkage for each drug, thereby enhancing statistical power. We develop efficient Markov chain Monte Carlo algorithms for implementation and employ a false discovery rate (FDR)‐controlled, false negative rate (FNR)‐optimized hypothesis testing framework for AE signal detection. Extensive simulations demonstrate our methods' superior sensitivity—often surpassing existing approaches by a twofold or greater margin—while strictly controlling the FDR. An application to FDA FAERS data on statin drugs further highlights our methods' effectiveness in real‐world AE signal detection. Software implementing our methods is provided as supplementary material.

Monitoring the safety of medical products is a core concern of contemporary pharmacovigilance. To support drug safety assessment, Spontaneous Reporting Systems (SRS) collect reports of suspected adverse events of approved medical products offering a critical resource for identifying potential safety concerns that may not emerge during clinical trials. Modern nonparametric empirical Bayes methods are flexible statistical approaches that can accurately identify and estimate the strength of the association between an adverse event and a drug from SRS data. However, there is currently no comprehensive and easily accessible implementation of these methods. Here, we introduce the R package pvEBayes, which implements a suite of nonparametric empirical Bayes methods for pharmacovigilance, along with post-processing tools and graphical summaries for streamlining the application of these methods. Detailed examples are provided to demonstrate the application of the package through analyses of two real-world SRS datasets curated from the publicly available FDA FAERS database.

Inferring adverse events (AEs) of medical products from Spontaneous Reporting Systems (SRS) databases is a core challenge in contemporary pharmacovigilance. Bayesian methods for pharmacovigilance are attractive for their rigorous ability to simultaneously detect potential AE signals and estimate their strengths/degrees of relevance. However, existing Bayesian and empirical Bayesian methods impose restrictive parametric assumptions and/or demand substantial computational resources, limiting their practical utility. This paper introduces a suite of novel, scalable empirical Bayes methods for pharmacovigilance that utilize flexible non‐parametric priors and custom, efficient data‐driven estimation techniques to enhance signal detection and signal strength estimation at a low computational cost. Our highly flexible methods accommodate a broader range of data and achieve signal detection performance comparable to or better than existing Bayesian and empirical Bayesian approaches. More importantly, they provide coherent and high‐fidelity estimation and uncertainty quantification for potential AE signal strengths, offering deeper insights into the comparative importance and relevance of AEs. Extensive simulation experiments across diverse data‐generating scenarios demonstrate the superiority of our methods in terms of accurate signal strength estimation, as measured by replication root mean squared errors. Additionally, our methods maintain or exceed the signal detection performance of state‐of‐the‐art techniques, as evaluated by frequentist false discovery rates and sensitivity metrics. Applications on FDA FAERS data for the statin group of drugs reveal interesting insights through Bayesian posterior probabilities.

A suite of empirical Bayes methods to use in pharmacovigilance. Contains various model fitting and post-processing functions. For more details see Tan et al. (2025) , Koenker and Mizera (2014) and Efron (2016) .

We developed an automated photoacoustic and ultrasound breast tomography system that images the patient in the standing pose. The system, named OneTouch-PAT, utilized linear transducer arrays with optical-acoustic combiners for effective dual-modal imaging. During scanning, subjects only need to gently attach their breasts to the imaging window, and co-registered three-dimensional ultrasonic and photoacoustic images of the breast can be obtained within one minute. Our system has a large field of view of 17 cm by 15 cm and achieves an imaging depth of 3 cm with sub-millimeter resolution. A three-dimensional deep-learning network was also developed to further improve the image quality by improving the 3D resolution, enhancing vasculature, eliminating skin signals, and reducing noise. The performance of the system was tested on four healthy subjects and 61 patients with breast cancer. Our results indicate that the ultrasound structural information can be combined with the photoacoustic vascular information for better tissue characterization. Representative cases from different molecular subtypes have indicated different photoacoustic and ultrasound features that could potentially be used for imaging-based cancer classification. Statistical analysis among all patients indicates that the regional photoacoustic intensity and vessel branching points are indicators of breast malignancy. These promising results suggest that our system could significantly enhance breast cancer diagnosis and classification.

Inferring adverse events (AEs) of medical products from Spontaneous Reporting Systems (SRS) databases is a core challenge in contemporary pharmacovigilance. Bayesian methods for pharmacovigilance are attractive for their rigorous ability to simultaneously detect potential AE signals and estimate their strengths/degrees of relevance. However, existing Bayesian and empirical Bayesian methods impose restrictive parametric assumptions and/or demand substantial computational resources, limiting their practical utility. This paper introduces a suite of novel, scalable empirical Bayes methods for pharmacovigilance that utilize flexible non-parametric priors and custom, efficient data-driven estimation techniques to enhance signal detection and signal strength estimation at a low computational cost. Our highly flexible methods accommodate a broader range of data and achieve signal detection performance comparable to or better than existing Bayesian and empirical Bayesian approaches. More importantly, they provide coherent and high-fidelity estimation and uncertainty quantification for potential AE signal strengths, offering deeper insights into the comparative importance and relevance of AEs. Extensive simulation experiments across diverse data-generating scenarios demonstrate the superiority of our methods in terms of accurate signal strength estimation, as measured by replication root mean squared errors. Additionally, our methods maintain or exceed the signal detection performance of state-of-the-art techniques, as evaluated by frequentist false discovery rates and sensitivity metrics. Applications on FDA FAERS data for the statin group of drugs reveal interesting insights through Bayesian posterior probabilities.

Objectives: Dental no‐show rates negatively impact oral health outcomes, especially among adolescents. While many factors can be associated with no‐show in dental appointments, adolescents’ no‐show rates are influenced by parents’ or caregivers’ experiences and decisions. This study aims to investigate patient visit‐related characteristics that are associated with failure to attend scheduled dental appointments in an academic institution located in Buffalo, New York (NY), a region impacted by health disparities. Methods: A secondary analysis was performed with multivariables extracted from the electronic health records of individuals aged 0–19 years from 2018 to 2023. Bivariate and multivariate analyses at the visit level were performed to compare show/no‐show groups concerning demographics, payor at the time of dental appointment, distance from home to dental facility, and Social Deprivation Index (SDI) scores related to their ZIP codes. Results: A total of 7379 visits were included in the analysis. 14.3% were no‐shows. Adolescents aged 12–17 years accounted for the greatest no‐show rate (24%) when compared with younger children. Age had an increased likelihood of no‐show (OR = 1.13, CI = 1.11–1.15), and social deprivation had a slight protective effect in no‐show at dental appointments in this academic institution (OR = 0.98, CI = 0.98–0.99). The great majority of pediatric patients come from socioeconomically deprived areas and live further away from the school. Conclusions: Adolescents are the group at most risk of no‐show. Future studies should explore strategies to better understand the barriers related to this lifespan as well as implement interventions that facilitate scheduling as well as prevent no‐shows.