BMC Medical Research Methodology

BackgroundJournals may respond to integrity concerns by publishing an editorial response (editorial notice, expression of concern (EoC) or retraction). We investigated whether the type of editorial response affected citation rates. MethodsWe obtained citations for 172 randomised controlled trials (RCTs) with integrity concerns (41 had editorial notices, 38 EoCs and 23 retractions) and control RCTs from the same journal and year. Monthly citation rates up to 60 months before and after editorial ...

The growth of generative AI and easily available Open Access health datasets has transformed researcher productivity, leading to an explosion in publications that has in part been attributed to paper mills (organisations that provide manuscripts for payment) and other unethical actors. These entities are not, however, homogenous, and have a range of products and target markets. While the demand from China has received much attention, here we provide a case study of CDC WONDER, a dataset that has...

Mixed Models for Repeated Measures (MMRM) are widely used in neurological clinical trials, including Alzheimers disease studies, due to their robust statistical properties and regulatory acceptance. However, traditional MMRM treats time as a categorical variable, limiting its ability to incorporate unscheduled visits, harmonize trials with different visit schedules, or handle densely collected data from digital health technologies. To address these limitations, we propose a Linear B-Spline-based...

BackgroundRoutinely collected health data are increasingly used to generate real-world evidence for therapeutic decision-making. Yet, stakeholders, including clinicians, pharmaceutical industry representatives, patient advocacy groups, and statisticians, prioritize different aspects of data quality, analysis, and interpretation. Without explicit consideration of these perspectives, analyses risk being fragmented, misaligned with end-user needs, or lacking transparency. MethodsWe developed a sta...

BackgroundIn pharmacoepidemiological studies, days of treatment (DoT) duration associated with individual electronic drug utilization records (DUR) are usually missing. Researcher-defined duration (RDD) calculation approaches, as opposed to data-driven approaches, can be used to estimate DoT based on the specific choices and assumptions made by investigators. These are usually underreported or even undocumented. We aimed to develop a framework for the standardization of terminology, formulas, im...

BackgroundThe ability of large language models (LLMs) to work collaboratively and screen studies in a systematic review (SR) is under-explored. Hence, we aimed to evaluate the effectiveness of LLMs in automating the process of screening in systematic reviews. MethodsThis is an observational study which included labeled data (title and abstracts) for five SRs. Originally, two reviewers screened the citations independently for eligibility. A third reviewer cross-checked each citation for quality ...

ObjectiveTo address the inefficiency, subjectivity, and high expertise barrier of traditional epidemiological causal inference, this study designed, developed, and validated an AI-powered agent (EpiCausalX Agent) to automate the end-to-end workflow. It integrates cross-database literature retrieval, intelligent causal reasoning, and Directed Acyclic Graph (DAG) visualization to provide a reliable, accessible tool for researchers. Materials and MethodsBuilt on the LangChain 1.0 framework with a ...

BackgroundThe number of problematic randomized clinical trials (RCTs) has risen sharply in recent decades, posing serious challenges to the integrity of the healthcare evidence ecosystem. ObjectiveTo investigate whether retraction of problematic RCTs could reduce evidence contamination. DesignRetrospective cohort study SettingA secondary analysis of the VITALITY Study database. Participants1,330 retracted RCTs with 847 systematic reviews. MeasurementsThe difference in the median number (and...

BackgroundThe exponential growth of Mendelian randomization (MR) literature has created challenges for systematically organising and synthesising evidence, with key information fragmented across heterogeneous publications. We present MR-KG, a knowledge graph resource using large language models (LLMs) to systematically extract and structure published MR evidence at scale. MethodsWe evaluated eight OpenAI and local LLMs for extracting structured information from MR study abstracts. Two reviewers...

The Health Insurance Review and Assessment Service Korean Nationwide Claims OMOP-CDM database (HIRA K-OMOP) is a nationwide data resource formatted according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and derived from South Koreas National Health Insurance claims data. It includes patient-level information and insurance claims for the entire population of South Korea from 2015 to 2024, providing population-scale coverage (56,416,773 patients). The HIRA K-OMO...

BackgroundWhen creating risk prediction models for time-to-event data, methods that incorporate time are typically used. Random survival forests (RSF), an extension of random forests (RF), are one such class of models. We compared RSF to RF in the context of time-to-event outcomes in the ASPirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial. We hypothesize that RSF will have superior discrimination and calibration versus RF. MethodsParticipants from ASPREE residing outs...

BackgroundTraditional pharmacovigilance methods based on biostatistical approaches systematically exclude outliers and rare events, potentially missing critical safety signals. These methods fail to detect micro-clusters of adverse events and comorbidity patterns that may indicate serious but low-frequency adverse drug reactions (ADRs). We introduce the concept of absurdity signal detection - the identification of statistically anomalous but clinically significant adverse event patterns that co...

BackgroundGenerating synthetic data using artificial intelligence, such as large language models (LLMs), is a useful strategy in public health because it can reduce time and costs, expand access to data, and facilitate information sharing without compromising confidentiality. ObjectiveTo evaluate the consistency and psychometric plausibility of synthetic data generated by an LLM to simulate the responses of survey participants (user personas) in a national health survey in Peru. MethodsWe cond...

Hybrid controlled trials (HCTs) incorporate real-world data into randomized controlled trials (RCTs) by augmenting the internal control arm with patients receiving the same treatment in routine care. Beyond increasing power, HCTs may improve recruitment by supporting unequal randomization ratios that increase patient access to experimental treatments. However, HCT validity is threatened by bias from unmeasured confounding due to lack of randomization of external controls, leading to outcome non-...

BackgroundClinical trial statistical programming is transitioning from manual, study-specific coding toward metadata-driven, automated pipelines. Although general data management transformation has been reviewed, comprehensive synthesis of statistical programming automation--particularly tables, listings, and figures (TLF) generation and validation frameworks--remains limited. This review addresses this gap through systematic evidence synthesis. MethodsWe conducted a structured literature revie...

The increasing use of Real-World Data (RWD) in clinical research is critical for evidence-based decision making but presents challenges to data analytics. Unlike in Randomized Controlled Trials (RCTs), missingness in RWD occurs often and can include complex patterns which may or may not be Missing at Random (MAR). Informative absence (Missing Not at Random, or MNAR) occurs when the absence of data itself is a clinical signal. Applications of ad-hoc methods, or even popular "universal" methods, c...

Systematic reviews are used in academia, biotechnology, pharmaceutical companies and government to synthesise and appraise large numbers of publications. The current (largely manual) workflow takes an average of 9-18 months1, at a cost of $100,000+ per review2. We built a platform, ScholaraAI, that leverages artificial intelligence to cut this to < 0.1% of the time, without compromising quality. ScholaraAI facilitates end-to-end systematic reviews; search, screening, data extraction, and analysi...

ObjectiveOur goal is to unify the 72 biomedical publication types and study designs (collectively, PTs) into a single rubric and hierarchy. Materials and MethodsThis is carried out in a data-driven manner by computing pairwise similarities of each PT against all others to form a similarity matrix. By performing hierarchical clustering we place each PT in a specific category and collect these into broader categories. ResultsSpearman correlations among PT pairs ranged from strongly negative to s...

PurposeLarge language models (LLMs) are used for biomedical text processing, but individual decisions are often hard to audit. We evaluated whether enforcing a mechanically checkable "show your work" quote affects accuracy, stability, and verifiability for trial eligibility-scope classification from abstracts. MethodsWe used 200 oncology randomized controlled trials (2005 - 2023) and provided models with only the title and abstract. Trials were labeled with whether they allowed for the inclusio...

BackgroundIndividual participant data (IPD) meta-analyses obtain, harmonise and synthesise the raw individual-level data from multiple studies, and are increasingly important in an era of data sharing and personalised medicine to inform clinical practice and policy. Objectives(1) Describe the landscape of IPD meta-analysis of randomised trials over time; (2) establish current practice in design, conduct, analysis and reporting for pairwise IPD meta-analysis; and (3) derive recommendations to i...