PURPOSE:This study aimed to identify and synthesize published algorithms for identifying the initiation of a new line of therapy (LOT) for metastatic lung, breast, and colorectal cancer in real-world data (RWD). METHODS:We conducted a scoping review of published, English-language studies describing algorithms for identifying any LOTs with systemic anti-cancer therapy (SACT) for either non-metastatic or metastatic lung, breast, or colorectal cancer in RWD between January 1, 2014, and April 29, 2024. Dual reviewers independently screened titles, abstracts, and full-text articles, with disagreements resolved by a third reviewer. Data were extracted, categorized, synthesized, and summarized in narrative and tabular formats. RESULTS:The review identified 25 studies, mainly (64%) from the United States. Electronic health/medical records (EHRs) were the most frequently utilized (72%) RWD source. Twenty-four studies (96%) described RWD algorithms for identifying the initiation of a new LOT for metastatic lung, breast, or colorectal cancer. In 23 studies, algorithms required observing a new, adjuvant, SACT after an "incident" metastatic diagnosis code, which had been preceded by a metastasis-free lookback period of varied duration. Three studies' algorithms required observation of the completion of non-metastatic LOTs before initiation of a new LOT for metastatic cancer. Three studies validated their algorithms. CONCLUSIONS:Different algorithms are being used to identify LOT initiation for metastatic cancer. Most algorithms require an incident diagnosis of metastasis before considering subsequent SACT as newly initiated LOT for metastatic cancer. However, definitions of metastasis onset and gap duration to therapy initiation vary.

ABSTRACT The Problem Transportability considerations are increasingly important to answer research questions in comparative effectiveness research (CER) to support the transfer of evidence on medicinal products across countries or settings. As drug development costs rise and healthcare systems and professionals face economic and resource pressures, leveraging existing data and evidence generated across borders or settings can improve efficiency and inform decisions in product development and decision‐making (regulatory, health technology assessment [HTA] and clinical care). Differences in population characteristics, healthcare systems, and data availability, including coding discrepancies, may present significant challenges to the transportability of CER evidence. Without rigorous methodological approaches, the utility of transportability analyses is limited. What we Did This article provides a structured framework for considering transportability exercises in CER analyses. We outlined key methodological principles, when and why to use transportability exercises in CER, feasibility assessments including effect modifier identification and causal inference techniques such as weighting, outcome regression, and combined methods to guide transportability analytical approaches in CER. By synthesizing existing literature and expert insights, we identified opportunities and trade‐offs in applying transportability methods to support decisions across the product lifecycle. Strategies to Disseminate and Facilitate Use To enhance the adoption of transportability analyses, we proposed best practices for researchers, regulators, HTA bodies, and industry stakeholders. These include early engagement with regulatory agencies and HTA bodies, transparent documentation of data assumptions, quality, fitness, and comparability assessments while ensuring robust analytical approaches. We also emphasized the need for standardized reporting guidelines and cross‐country collaborations to validate transportability methods in real‐world settings and communicate uncertainty in transported evidence. Conclusions Transportability analyses offer a powerful tool for extending the applicability of CER findings across healthcare systems, improving evidence generation efficiency, and supporting global drug development and evaluation. By implementing best practices that promote a rigorous and transparent approach to the design and conduct of such analyses, stakeholders can maximize the value of transported treatment effects while ensuring scientific rigor and decision‐making relevance. Future research should focus on empirical testing and validation of transportability methods targeting different questions across the product lifecycle and the development of harmonized regulatory and HTA methodological standards. “This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).” Official Endorsement was received on 5/13/26.

The Problem Transportability considerations are increasingly important to answer research questions in comparative effectiveness research (CER) to support the transfer of evidence on medicinal products across countries or settings. As drug development costs rise and healthcare systems and professionals face economic and resource pressures, leveraging existing data and evidence generated across borders or settings can improve efficiency and inform decisions in product development and decision-making (regulatory, health technology assessment [HTA] and clinical care). Differences in population characteristics, healthcare systems, and data availability, including coding discrepancies, may present significant challenges to the transportability of CER evidence. Without rigorous methodological approaches, the utility of transportability analyses is limited.What we Did This article provides a structured framework for considering transportability exercises in CER analyses. We outlined key methodological principles, when and why to use transportability exercises in CER, feasibility assessments including effect modifier identification and causal inference techniques such as weighting, outcome regression, and combined methods to guide transportability analytical approaches in CER. By synthesizing existing literature and expert insights, we identified opportunities and trade-offs in applying transportability methods to support decisions across the product lifecycle.Strategies to Disseminate and Facilitate Use To enhance the adoption of transportability analyses, we proposed best practices for researchers, regulators, HTA bodies, and industry stakeholders. These include early engagement with regulatory agencies and HTA bodies, transparent documentation of data assumptions, quality, fitness, and comparability assessments while ensuring robust analytical approaches. We also emphasized the need for standardized reporting guidelines and cross-country collaborations to validate transportability methods in real-world settings and communicate uncertainty in transported evidence.Conclusions Transportability analyses offer a powerful tool for extending the applicability of CER findings across healthcare systems, improving evidence generation efficiency, and supporting global drug development and evaluation. By implementing best practices that promote a rigorous and transparent approach to the design and conduct of such analyses, stakeholders can maximize the value of transported treatment effects while ensuring scientific rigor and decision-making relevance. Future research should focus on empirical testing and validation of transportability methods targeting different questions across the product lifecycle and the development of harmonized regulatory and HTA methodological standards. "This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE)." Official Endorsement was received on 5/13/26.

Introduction Artificial intelligence (AI) is rapidly evolving, offering an expanding suite of capabilities that go beyond the traditional focus on prediction and classification. Generative AI (GenAI) and agentic AI could create transformative practices to support real-world evidence (RWE) generation for health research by streamlining studies, accelerating insights and improving decision-making. However, there is no published overview available describing the range of applications in RWE generation. This review aims to describe where and how genAI and agentic AI are applied across the domains of healthcare research tasks for RWE generation. Additionally, to map applications by tasks and methods across the product lifecycle continuum, and to identify emerging gaps and opportunities.Methods and analysis This Living Scoping Review (LSR) will include studies reporting an application and/or evaluation of genAI or agentic AI applied to one or more RWE generation research tasks. Searches will be conducted in Embase, MEDLINE and additional sources (eg, grey literature). Citations will be independently screened by two human senior reviewers for a substantive training dataset and a commercially available screening algorithm (Robot Screener) will complete screening with a human reviewer. The LSR will include reports of studies (primary or reviews) describing and/or evaluating the application of any genAI model for RWE generation in healthcare, in English, published from 1 January 2025 to the date of search. Data will be extracted from all studies included in the LSR by one independent senior reviewer using a piloted template, with 10% quality check by a second senior reviewer. Descriptive statistics will be used to summarise the applications of genAI per RWE research task, and the results of genAI evaluations. Thematic analysis will be used to describe genAI application patterns, trends, gaps and opportunities. The LSR protocol and reports will be updated annually, and findings will be published on a publicly available website (eg, ISPE—the International Society for Pharmacoepidemiology).Ethics and dissemination Ethical approval is not required due to use of previously published data. Planned dissemination includes peer-reviewed publication, presentation and short summaries.

The ICH E9(R1) estimand framework provides a systematic approach to ensure alignment among clinical trial objectives, trial conduct, statistical analyses, and interpretation of results, however, whether it can be readily utilized for the pharmacoepidemiologic safety studies has not been established. We selected articles on drug safety published in the Journal Pharmacoepidemiology and Drug Safety (PDS), during 2020 to investigate whether estimand attributes were well defined in the study design and reporting. We found that among twenty-five articles selected, nineteen were cohort studies and six were nested case-control studies. All studies had well-defined exposure, outcome, target population, and population level summary. The term intercurrent event (ICE) was not mentioned in any of the studies; however, many cohort studies discussed drug discontinuation, treatment modification and terminal events, and strategies to handle them. All studies used methods to control for confounding: propensity score methods or covariate adjustment, or both for cohort studies; matching and covariate adjustment for the nested case-control studies. We conclude that while the estimand framework can serve to add clarity and precision to pharmacoepidemiologic safety studies, more detailed considerations are required for bias assessment to compensate for the lack of randomization and other shortcomings in observational studies. Recent pharmacoepidemiology frameworks, such as Target Trial Emulation, STaRT-RWE, HARPER could be combined with the complementary principals from the estimand framework to help achieve the study objectives.

PURPOSE:Pharmacoepidemiologic studies on deprescribing are challenging to implement, yet little guidance exists on methods to avoid bias and minimum reporting for replicability and appraisal. We developed consensus recommendations for the methods and reporting of observational studies that aim to examine the effects of deprescribing. METHODS:We formed candidate recommendations based on our prior systematic review that methodologically appraised observational studies on deprescribing. We then conducted a two-round modified Delphi process with researchers working in deprescribing pharmacoepidemiology to refine, select, and reach consensus on recommendations for a checklist based on > 70% agreement of their importance. We termed this list the REMROSE-D (Reporting and Methodological Recommendations for Observational Studies estimating the Effects of Deprescribing medications) guidance. RESULTS:Twenty-three candidate recommendations were presented to the Delphi panel. The round 1 survey was completed by 55 participants, and 18 of the 23 candidate recommendations were selected for inclusion. Five candidate recommendations without consensus plus two additional items suggested by participants were included in a round 2 survey of 25 deprescribing researchers. Five of these seven items garnered consensus for inclusion, and two were excluded. The final REMROSE-D guidance contains 23 recommendations for the methods and reporting of observational research on deprescribing. CONCLUSION:To ensure rigor and reproducibility in observational studies of the effects of deprescribing, the REMROSE-D guidance provides recommendations for important reporting and methods considerations, including time zero, precise definitions of deprescribing, addressing confounding by indication, and careful consideration of follow-up to avoid immortal time bias.

Observational studies using real-world data (RWD) can address gaps in knowledge on deprescribing medications but are subject to methodological issues. Limited data exist on the methods employed to use RWD to measure the effects of deprescribing. To describe methodological approaches used in observational studies of deprescribing medications in older adults, we conducted a systematic review in Medline for observational studies published in English (January 1, 2000, to September 14, 2023) that examined the health effects of medication deprescribing in older adults. We described study characteristics and methods, focusing on the operationalization of deprescribing as an exposure and potential time-related biases. Forty-five studies were included, representing a variety of drug classes (eg, statins, aspirin, bisphosphonates) and diseases. Most studies adequately addressed potential time-related biases. The definition of deprescribing was not clearly defined in 12 studies. There was heterogeneity regarding the minimum duration of time that qualified as deprescribing, even within a drug class; fewer than one-third of studies provided a justification for these definitions. Observational studies are common to examine the effects of deprescribing; however, there were inconsistencies in measuring deprescribing and a lack of transparency in reporting. There is a need for minimum sufficient reporting criteria for observational studies on deprescribing.

Comprehensible reporting of clinical trial safety data is essential for multiple stakeholders, including ongoing safety reporting by sponsors to the health authorities. However, the consistency and completeness of information about safety events of interest (EOIs) in public sources is not well characterized. This study examined the availability and transparency of adverse event (AE) information from public clinical trial data sources, with a focus on their utility in similar patient populations for signal detection and contextualizing rates of anticipated EOI reported as serious or severe (grade ≥ 3). A structured review of 44 EOI for ten medicinal products approved for different indications in the past 7 years in the United States (US) and European Union was conducted. The selected EOIs were events likely to be reported as serious or severe in clinical trials and additionally were either anticipated AEs or of customary high interest for the patient population. Safety data from journal publications, ClinicalTrials.gov, US Food and Drug Administration (FDA) review documents, European Public Assessment Reports (EPARs), and product labeling (US Prescribing Information, Summary of Product Characteristics) were evaluated. Parameters assessed for availability included demographic data, disease severity measures, serious AE (SAE) frequency, and exposure-adjusted rates. Clarity in approach for EOI identification also was evaluated, specifically whether based on expert adjudication or delineated pre-specified or ad hoc groupings of Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs) or alternatively not specified. Overall, clinical summaries available at Drugs@FDA and EPARs provided the most information about enrolled trial participant characteristics. Greater than 95

Pregnant women and newborns are historically underrepresented in clinical trials, creating critical gaps in evidence on the safety and effectiveness of medications used during pregnancy. Real-world data (RWD) sources offer a promising avenue to address these gaps. To fully realize this potential, it is essential to link maternal and infant records accurately within and across diverse datasets. High-quality mother--infant linkage enables the robust evaluation of maternal medication use and its short- and long-term effects on both maternal and infant health. However, linking maternal and infant healthcare data introduces complex methodological and practical challenges. Achieving accurate linkage is often hindered by factors such as inconsistent personal identifiers, discrepancies in insurance coverage between mother and infant, data incompleteness, algorithmic accuracy, and strict data privacy regulations. Commonly used proxies for linkage (e.g. shared address or healthcare provider) may also be unreliable and can introduce misclassification or duplication. This commentary synthesizes current knowledge on mother--infant data linkage in RWD. It also systematically outlines the key challenges, emerging opportunities, and strategic directions to improve linkage quality and address privacy concerns in the identification of mother--infant dyads to support rigorous pharmacoepidemiologic research on maternal and infant health outcomes. By improving linkage methods and leveraging innovative approaches such as tokenization and validated algorithms, researchers can enhance the reliability of real-world evidence on maternal and infant health outcomes, including long-term follow-up across diverse data sources. Advancing these methodological frontiers is essential to generate evidence that supports safer and more informed treatment decisions for pregnant women and their children.

PURPOSE:Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence. METHODS:In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes. RESULTS:Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization. CONCLUSIONS:The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

Screening for drug-induced hyperprolactinaemia, a condition characterised by higher-than-normal levels of serum prolactin induced by drug treatments, requires a comprehensive understanding of the clinical presentations and long-term complications of the condition. Using two databases, Embase and MEDLINE, we summarised the available evidence on the clinical presentations and long-term complications of drug-induced hyperprolactinaemia. Clinical and observational studies reporting on drug treatments known or suspected to induce hyperprolactinaemia were included. Database searches were limited to the English language; no date or geographic restrictions were applied. Fifty studies were identified for inclusion, comprising a variety of study designs and patient populations. Most data were reported in patients treated with antipsychotics, but symptoms were also described among patients receiving other drugs, such as prokinetic drugs and antidepressants. Notably, the diagnosis of drug-induced hyperprolactinaemia varied across studies since a standard definition of elevated prolactin levels was not consistently applied. Frequent clinical presentations of hyperprolactinaemia were menstrual cycle bleeding, breast or lactation disorders, and sexual dysfunctions, described in 80% (40/50), 74% (37/50), and 42% (21/50) of the included studies, respectively. In the few studies reporting such symptoms, the prevalence of vaginal dryness impacted up to 53% of females, and infertility in both sexes ranged from 15 to 31%. Clinicians should be aware of these symptoms related to drug-induced hyperprolactinaemia when treating patients with drugs that can alter prolactin levels. Future research should explore the long-term complications of drug-induced hyperprolactinaemia and apply accepted thresholds of elevated prolactin levels (i.e., 20 ng/mL for males and 25 ng/mL for females) to diagnose hyperprolactinaemia as a drug-induced adverse event. Trial Registration PROSPERO International Prospective Register Of Systematic Reviews (CRD42021245259).

Aggregate safety assessment involves evaluation of the totality of safety data to characterize the emerging safety profile of a product. The Drug Information Association–American Statistical Association Interdisciplinary Safety Evaluation scientific working group recently published an approach to developing an Aggregate Safety Assessment Plan (ASAP). Creation of an ASAP facilitates a consistent approach to safety data collection and analysis across studies and minimizes important missing data at the time of regulatory submission. A critical aspect of the ASAP is identification of the Safety Topics of Interest (STOI). The STOI, as defined in the ASAP, comprises adverse events (AEs), which have the potential to impact the benefit: risk profile of a product and typically require specialized data collection or analyses. While there are clear benefits to developing an ASAP for a drug development program, multiple concerns may be encountered with implementation. This article uses the examples of two STOIs to demonstrate the benefits and efficiencies gained with implementation of the ASAP in safety planning as well as in optimally characterizing the emerging safety profile of a product.

This systematic literature review (SLR) assessed incidence/prevalence of cryptoglandular fistulas (CCF) and outcomes associated with local surgical and intersphincteric ligation procedures for CCFs. Two trained reviewers searched PubMed and Embase for observational studies evaluating the incidence/prevalence of cryptoglandular fistula and clinical outcomes of treatments for CCF after local surgical and intersphincteric ligation procedures for CCF. In total 148 studies met a priori eligibility criteria for all cryptoglandular fistulas and all intervention types. Of those, two assessed incidence/prevalence of cryptoglandular fistulas. Eighteen reported clinical outcomes of surgeries of interest in CCF and were published in the past 5 years. Prevalence was reported as 1.35/10,000 non-Crohn’s patients, and 52.6

PURPOSE:Given limited information available on real-world data (RWD) sources with pediatric populations, this study describes features of globally available RWD sources for pediatric pharmacoepidemiologic research.METHODS:An online questionnaire about pediatric RWD sources and their attributes and capabilities was completed by members and affiliates of the International Society for Pharmacoepidemiology and representatives of nominated databases. All responses were verified by database representatives and summarized.RESULTS:Of 93 RWD sources identified, 55 unique pediatric RWD sources were verified, including data from Europe (47%), United States (38%), multiregion (7%), Asia-Pacific (5%), and South America (2%). Most databases had nationwide coverage (82%), contained electronic health/medical records (47%) and/or administrative claims data (42%) and were linkable to other databases (65%). Most (71%) had limited outside access (e.g., by approval or through local collaborators); only 10 (18%) databases were publicly available. Six databases (11%) reported having >20 million pediatric observations. Most (91%) included children of all ages (birth until 18th birthday) and contained outpatient medication data (93%), while half (49%) contained inpatient medication data. Many databases captured vaccine information for children (71%), and one-third had regularly updated data on pediatric height (31%) and weight (33%). Other pediatric data attributes captured include diagnoses and comorbidities (89%), lab results (58%), vital signs (55%), devices (55%), imaging results (42%), narrative patient histories (35%), and genetic/biomarker data (22%).CONCLUSIONS:This study provides an overview with key details about diverse databases that allow researchers to identify fit-for-purpose RWD sources suitable for pediatric pharmacoepidemiologic research.

Dopamine antagonists are the main pharmacological options to treat gastroparesis. The aim of this study was to conduct a systematic literature review (SLR) to evaluate the profile of adverse events (AEs) of dopamine antagonists used in the treatment of children and adults with gastroparesis. We searched EMBASE and MEDLINE up to March 25, 2021, for relevant clinical trials and observational studies. We conducted a proportional meta-analysis to estimate the pooled occurrence of AEs (