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Purpose: Prevalent new user (PNU) designs extend the active comparator new user design by allowing for the inclusion of initiators of the study drug who were previously on a comparator treatment. We performed a literature review summarising current practice.
Methods: PubMed was searched for studies applying the PNU design since its proposal in 2017. The review focused on three components. First, we extracted information on the overall study design, including the database used. We summarised information on implementation of the PNU design, including key decisions relating to exposure set definition and estimation of time-conditional propensity scores. Finally, we reviewed the analysis strategy of the matched cohort.
Results: Nineteen studies met the criteria for inclusion. Most studies (73%) implemented the PNU design in electronic health record or registry databases, with the remaining using insurance claims databases. Of 15 studies including a class of prevalent users, 40% deviated from the original exposure set definition proposals in favour of a more complex definition. Four studies did not include prevalent new users but used other aspects of the PNU framework. Several studies lacked details on exposure set definition (n = 2), time-conditional propensity score model (n = 2) or integration of complex analytical techniques, such as the high-dimensional propensity score algorithm (n = 3).
Conclusion: PNU designs have been applied in a range of therapeutic and disease areas. However, to encourage more widespread use of this design and help shape best practice, there is a need for improved accessibility, specifically through the provision of analytical code alongside guidance to support implementation and transparent reporting.
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http://dx.doi.org/10.1002/pds.5656 | DOI Listing |
Comput Biol Med
August 2025
Faculty of Computers and Information Science, Mansoura University, Egypt; Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, USA. Electronic address:
Colorectal cancer (CRC) is a major global health concern, where timely and precise diagnosis is crucial for effective treatment. In medical imaging, accurate segmentation of pathological regions is essential for guiding diagnostic decisions and treatment strategies. However, traditional metaheuristic-based segmentation methods often face challenges like slow convergence, suboptimal threshold determination, and inadequate balancing between exploration and exploitation, which can limit their effectiveness in multi-threshold image segmentation (MTIS) of CRC pathology images.
View Article and Find Full Text PDFSci Rep
August 2025
Department of Computer and Self-Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia.
Human Activity Recognition (HAR) has become an active research area in recent years due to its applicability in various domains and the growing need for convenient facilities and intelligent homes for the elderly. Physical activity tends to decrease as people age, along with their ability to perform day-to-day tasks, which affects both mental and physical health. Several investigators apply deep learning (DL) and machine learning (ML) approaches to recognize human activities, but minimal investigations are concentrated on human activity recognition of older adults.
View Article and Find Full Text PDFSci Rep
August 2025
EIAS Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
With the exponential growth of big data in domains such as telemedicine and digital forensics, the secure transmission of sensitive medical information has become a critical concern. Conventional steganographic methods often fail to maintain diagnostic integrity or exhibit robustness against noise and transformations. In this study, we propose a novel deep learning-based steganographic framework that combines Squeeze-and-Excitation (SE) blocks, Inception modules, and residual connections to address these challenges.
View Article and Find Full Text PDFComput Biol Med
August 2025
Department of Pharmaceutical Chemistry, TeMS.C., Islamic Azad University, Tehran, Iran.
In this study, quantitative structure-activity relationship (QSAR) models were developed by the Monte Carlo technique to predict the anti-breast cancer activity of 144 novel 1,2-naphthoquinone and 1,4-naphthoquinone derivatives against MCF-7 breast cancer cells. To establish QSAR models, a balance of correlation techniques involving the index of ideality of correlation (IIC) and the correlation intensity index (CII), as well as an optimal hybrid descriptor derived from the integration of the Simplified Molecular Input Line Entry System (SMILES) and molecular hydrogen-suppressed graphs (HSG), was used. The resulting models provided valuable information about identifying molecular fragments that enhance or reduce biological activity.
View Article and Find Full Text PDFActa Psychol (Amst)
September 2025
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia. Electronic address:
This study intends to examine how personality factors affect people's risk-aversion behavior and investment intentions, as well as how risk aversion mediates the link between personality traits and investment intentions, and how gender acts as a moderator. Individuals have five different personality qualities in this study, which are openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism, while short-term and long-term investment intents are employed as investment intentions. The data is gathered from 750 participants (students) with backgrounds in business and finance.
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