BMC Medical Research Methodology最新文献

{"title":"Statistical inference after variable selection in Cox models: a neutral simulation study.","authors":"Lena Schemet, Sarah Friedrich-Welz","doi":"10.1186/s12874-026-02887-0","DOIUrl":"10.1186/s12874-026-02887-0","url":null,"abstract":"<p><p>Choosing relevant predictors is central to the analysis of biomedical time-to-event data. Classical frequentist inference, however, presumes that the set of covariates is fixed in advance and does not account for data-driven variable selection. As a consequence, naive post-selection inference may be biased and misleading. In right-censored survival settings, these issues may be further exacerbated by the additional uncertainty induced by censoring. We investigate several inference procedures applied after variable selection for the coefficients of the Lasso and its extension, the adaptive Lasso, in the context of the Cox model. The methods considered include sample splitting, post-selection inference procedures that condition explicitly on the Lasso selection event, and the debiased Lasso. Because these methods address different inferential targets, we distinguish selected-submodel targets from full-model targets and interpret empirical coverage, interval width, power, and type I error accordingly. Their performance is examined in a neutral simulation study reflecting realistic covariate structures and censoring rates commonly encountered in biomedical applications. The primary focus is post-selection inference after Cox-Lasso variable selection, not a comprehensive benchmark of very-high-dimensional variable-selection performance. To complement the simulation results, we illustrate the practical behavior of these procedures in an applied example using a publicly available survival dataset.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13289528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148307004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable nonlinear Cox modeling via random Fourier features with analytic uncertainty.","authors":"Fahrettin Kaya","doi":"10.1186/s12874-026-02925-x","DOIUrl":"10.1186/s12874-026-02925-x","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The Cox proportional hazards model often fails to capture complex biomedical risk structures, such as U-shaped biomarker associations, due to its assumption of linearity between the log-hazard and covariates. While existing kernel-based generalizations offer the necessary flexibility, their [Formula: see text] computational complexity limits applicability in large-scale cohort studies. Furthermore, most non-linear machine learning methods lack closed-form analytical measures of uncertainty for individual predictions..&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A novel Random Fourier Features-based Cox regression approach (RFF-Cox) is presented to model non-linear risk relationships within a scalable framework. By mapping stationary kernels into a finite-dimensional explicit feature space, the method reduces computational complexity to [Formula: see text]. Model parameters are estimated via the Newton-Raphson algorithm on a ridge-regularized partial likelihood, while the bandwidth parameter ([Formula: see text]) is automatically optimized using a marginal likelihood criterion based on the Laplace approximation. Uncertainty quantification is performed via the Fisher information matrix, with a multivariate Delta method propagating variance from both parameter estimation and baseline hazard estimation. A Taylor-expansion-based inference framework enables covariate-level hazard ratio estimation, Wald-type significance testing, and formal interaction detection. Performance was evaluated using controlled simulations and four real-world datasets with sample sizes ranging from 500 to 9,105.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In simulation scenarios, RFF-Cox degenerated to classical Cox estimates under linearity (RMSE: 0.111 vs. 0.110) while demonstrating a marked accuracy advantage under non-linearity (RMSE: 0.137 vs. 0.314), including the recovery of U-shaped risk functions. The true SBP × Smoking interaction was detected (estimate -1.093, p &lt; 0.001). Analytical 95% CI coverage reached 95.9% in the linear scenario and 81.9% in the non-linear scenario. In real-world applications, the model exhibited discriminatory power competitive with Random Survival Forests and XGBoost with favorable computational efficiency. IPCW-weighted calibration analyses on the METABRIC dataset yielded low Integrated Calibration Error (ICI &lt; 0.05) across most time horizons, though calibration slope deviations were noted at longer follow-up periods. Moreover, uncertainty in individual predictions, quantified via analytical confidence intervals, varied meaningfully across risk groups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;RFF-Cox provides a practical survival analysis framework that degenerates to the classical Cox model under linearity while offering non-linear modelling capabilities, computational efficiency, and formal inference tools - including covariate-level interpretation and interaction detection - that are not readily available in tree-based or deep learni","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13543420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148306931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the Cox model: a log-logistic accelerated failure time frailty model for analysing first birth timing in Zimbabwe.","authors":"Hilary Takunda Takawira","doi":"10.1186/s12874-026-02931-z","DOIUrl":"10.1186/s12874-026-02931-z","url":null,"abstract":"<p><strong>Background: </strong>The Cox proportional hazards (PH) model is the most common approach for analyzing time-to-event data in public health. However, its reliance on the proportional hazards' assumption is a key limitation, often violated in demographic research. This study illustrates a comprehensive model selection framework, comparing the performance of various parametric accelerated failure time (AFT) and frailty models as robust alternatives when the PH assumption fails, using data on the timing of first birth as a case study.</p><p><strong>Methods: </strong>We conducted a secondary analysis of time to first birth among 9,955 women from the 2015 Zimbabwe Demographic and Health Survey. After confirming violations of the PH assumption using Schoenfeld residuals, we systematically compared the fit of several parametric models' exponential, Weibull, log-normal, and log-logistic with and without gamma-shared frailty terms. Model selection was based on the Akaike (AIC) and Bayesian (BIC) Information Criteria.</p><p><strong>Results: </strong>The log-logistic AFT model with gamma frailty showed improved relative fit for this dataset, accommodating a non-monotonic hazard and accounting for unobserved regional heterogeneity (θ = 0.671, p<0.001). The model yielded interpretable time ratio (TR) estimates, where higher education (TR=1.11) and later cohabitation (TR=1.25) were associated with delayed first birth, while marriage (TR=0.98) and contraceptive use (TR=0.97) were associated with earlier timing. In contrast, standard parametric models and the Cox model were suboptimal for this dataset.</p><p><strong>Conclusion: </strong>This study provides an empirical demonstration of how AFT models with frailty terms can be applied in settings where the proportional hazards assumption is violated. The log-logistic frailty model showed improved relative fit for this dataset and offers an interpretable framework for analysing time-to-event outcomes with clustered data.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toolkit for multistate disease progression simulation and treatment decision-making aid.","authors":"Can Xie, Ruosha Li, Wael Saber, Jorge E Cortes, Nicholas J Short, Xuelin Huang","doi":"10.1186/s12874-026-02904-2","DOIUrl":"10.1186/s12874-026-02904-2","url":null,"abstract":"<p><strong>Background: </strong>The optimal timing of a treatment within therapy lines can maximize its benefit while minimizing risks and side effects. Because patients differ in clinical and biological characteristics, personalized treatment planning can support physicians' decision-making. However, multistate disease progression complicates the comparison of treatment strategies, motivating the development of a user-friendly web tool for multistate disease progression simulation and treatment decision support. Microsimulation is a natural approach for comparing prespecified treatment-timing strategies, where it generates disease trajectories for a large number of virtual patients matched to an individual patient's observed data. In our framework, microsimulation is performed using a multistate model to capture state-to-state transition hazards.</p><p><strong>Results: </strong>We develop TxMicroSim, a web-based interface built with R Shiny, to compare treatment-timing strategies by estimating model-projected restricted mean survival times (RMSTs). The application consists of three main steps: defining the multistate structure, building the multistate model, and performing microsimulation. First, the user specifies the multistate structure, with the treatment of interest as one of the states and an absorbing state representing the time-to-event endpoint. The user also identifies transitions that lead into the treatment state, defining the strategy-driven transitions used to compare treatment-timing strategies. Next, to build clock-reset transition-specific flexible parametric proportional hazards models, the user can either upload data for model fitting or manually enter covariate hazard ratios and baseline hazard parameters. Two baseline hazard specifications are supported: a piecewise constant function and a natural cubic spline. Finally, the user enters patient-specific information, including covariates, the initial disease state at diagnosis, and the treatment start time for strategy-driven transitions. Microsimulation is used to compute RMST up to a prespecified time horizon under each strategy. Throughout the app, interactive visualizations are provided for the multistate structure, estimated hazard ratios and baseline hazards, and RMST comparisons across treatment strategies.</p><p><strong>Conclusions: </strong>TxMicroSim is a flexible web tool that compares treatment-timing strategies and quantifies survival impact using interpretable summaries and interactive visualizations.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13543485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and recent advances in methods for handling imbalanced multiclass classification problems: a methodological review.","authors":"Sachin Acharya, Satyanarayana Poojari, Vani Lakshmi R, Asha Kamath","doi":"10.1186/s12874-026-02899-w","DOIUrl":"10.1186/s12874-026-02899-w","url":null,"abstract":"<p><strong>Background: </strong>Class imbalance is a common challenge in real-world health science applications, including medical diagnosis, rare disease detection, and ICU mortality prediction, where one or more classes are underrepresented. Although several methods address imbalance in binary classification, multiclass imbalance remains particularly challenging due to multiple minority classes, often leading to biased performance and reduced predictive accuracy. Despite several advancements, most classification models struggle to identify patterns in imbalanced data, limiting their effectiveness in real-world applications.</p><p><strong>Methods: </strong>A structured literature search was conducted to identify methodological studies on imbalanced multiclass classification, including algorithmic strategies and advances in performance evaluation. Articles published up to 2024 were retrieved from Scopus and Web of Science using predefined keywords. Studies were screened through titles and abstracts based on predefined inclusion and exclusion criteria, with additional backward citation searching for methodologically relevant studies. In total, 75 studies were included in the final methodological review to synthesize key challenges and recent advances.</p><p><strong>Results: </strong>Despite the introduction of several metrics for assessing multiclass imbalance, the Imbalance Ratio (IR) remains the most commonly used measure for quantifying imbalance severity. Existing balancing techniques mainly rely on distance-based, cluster-based, and distribution-based approaches, reflecting methodological diversity. In multiclass settings, various decomposition strategies, classification algorithms, and performance metrics have been proposed to address imbalance; however, repeated use of imbalance-handling mechanisms, such as class weight adjustments across decomposition, training, and evaluation stages, may introduce bias. The effectiveness of these strategies depends on data characteristics including dimensionality, sample size, distribution, number of classes, and imbalance severity. Notably, insufficient reporting of these characteristics in many studies limits the assessment of feasibility and generalizability across diverse data settings.</p><p><strong>Conclusions: </strong>This review synthesizes the strengths and limitations of existing methods for handling imbalanced multiclass classification, offering practical insights for improving model robustness and predictive performance. Effective management of class imbalance supports several Sustainable Development Goals by promoting equitable decision-making and enhancing reliable analysis across diverse health, societal, and environmental challenges, making it essential for developing robust and generalizable models across diverse domains.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13536707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148293306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thinking-aloud methodology applied to health questionnaire development and validation: a scoping review.","authors":"Laura Tasson, Marta Bonato, Farah D'Odorico, Matteo Danielis, Matteo Martinato, Cristina Canova","doi":"10.1186/s12874-026-02927-9","DOIUrl":"10.1186/s12874-026-02927-9","url":null,"abstract":"<p><strong>Background: </strong>The development of valid and reliable health questionnaires relies on respondents' accurate comprehension of items. The thinking-aloud (TA) technique, where participants verbalize their thought processes while answering survey questions, offers a powerful means to explore cognitive mechanisms underlying response behaviour. Despite its wide use, there is no consensus on how TA protocols should be designed, conducted, and analysed.</p><p><strong>Objective: </strong>This scoping review aimed to map methodological approaches applied to TA in health-related questionnaire development and validation, describe their variability, and critically examine current methodological practices to inform future research.</p><p><strong>Design: </strong>PubMed, Scopus, and Embase were searched on July 17, 2025, without time limits. Eligible studies were original articles in English applying TA for questionnaire validation, including assessment of content, face, construct validity, or comprehensibility. Two reviewers independently screened records and extracted data using a standardized charting form. An operational criterion was used to judge the completeness of methodological reporting. Data were synthesized narratively and tabulated to describe study purposes, TA typologies, analytical strategies, and theoretical frameworks.</p><p><strong>Results: </strong>From 1,678 retrieved records, 210 studies met inclusion criteria, of which 84 provided complete methodological descriptions. Most of the 84 studies involved patients (48%) and used concurrent TA (48%), with frequent prompting (55%) and probing (48%). Sessions were mainly face-to-face (71%), audio recorded (64%), and transcribed verbatim. Thematic (38%) and content (26%) analyses predominated, often supported by NVivo. The Survey Response Model was the most cited theoretical framework (26%), though 22% of studies lacked any theoretical reference. Coding strategies were mixed (36%), and about half of the studies reported questionnaire revisions after analysis.</p><p><strong>Conclusions: </strong>TA remains a versatile but methodologically fragmented approach in questionnaire validation. The lack of standardization limits comparability and interpretability across studies. Greater transparency in reporting and explicit theoretical framing are recommended to improve methodological rigor.</p><p><strong>Registration: </strong>The review followed the PRISMA-ScR framework and was prospectively registered on OSF (DOI: https://doi.org/10.17605/OSF.IO/AZY3 ).</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13640185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148293337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CACE-MM: using mixed methods to strengthen causal inference in medicine and public health.","authors":"Noor Qaragholi, Joseph J Gallo, Laura K Beres, Trang Q Nguyen, Sarah Walsh, Elizabeth A Stuart","doi":"10.1186/s12874-026-02908-y","DOIUrl":"10.1186/s12874-026-02908-y","url":null,"abstract":"<p><strong>Background: </strong>Causal inference in medicine and public health almost always depends on untestable assumptions. Estimating valid causal effects thus often requires substantive knowledge about the study context that quantitative methods alone cannot provide. This paper introduces CACE-MM, a mixed methods framework that integrates qualitative approaches with complier average causal effect (CACE) estimation to strengthen causal decision-making and assess the plausibility of key underlying assumptions. CACE-MM is the first framework to systematically integrate qualitative inquiry with causal effect estimation in the presence of noncompliance.</p><p><strong>Methods: </strong>We present a proof-of-concept application of CACE-MM using data from The Youth Empowerment Study (YES), a randomized trial of a trauma-informed intervention for youth involved in the juvenile legal system (n = 630). Following CACE analyses using an instrumental variable approach (invoking the exclusion restriction) and principal score approach (invoking the assumption of principal ignorability), we conducted 10 semi-structured interviews with key informants. Qualitative data were analyzed using inductive open coding followed by deductive mapping to the Capability, Opportunity, Motivation-Behavior (COM-B) model and the Theoretical Domains Framework (TDF). The study team assessed the plausibility of the key assumptions underlying each quantitative approach before and after the qualitative inquiry to generate integrated metainference about assumptions.</p><p><strong>Results: </strong>Qualitative findings identified predictors of participation and outcomes that were not captured in baseline quantitative measures, raising concerns about the plausibility of principal ignorability. Interviews also clarified how meaningful exposure to intervention components was understood by implementers, informing the defensibility of participation thresholds used to invoke the exclusion restriction. More broadly, the findings demonstrate how qualitative inquiry can inform key analytic decisions that shape causal estimates, including how participation is defined, which covariates should be prioritized for measurement, and whether particular identification strategies are appropriate for specific outcomes.</p><p><strong>Conclusions: </strong>Building on these insights, we propose the full CACE-MM framework, incorporating both exploratory and explanatory phases, and outline decision points to guide application in applied health research. CACE-MM offers a rigorous and systematic approach for integrating qualitative evidence into causal analyses and ultimately strengthening the transparency and interpretability of the CACE in applied health research.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT03242447.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13374248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reproducible statistical evaluation framework for large-sample assessment of AI-generated medical references: cross-platform application of the reference hallucination score.","authors":"Nurmuhammet Taş, Yakup Erden, Mustafa Hüseyin Temel, Fatih Bağcıer","doi":"10.1186/s12874-026-02906-0","DOIUrl":"10.1186/s12874-026-02906-0","url":null,"abstract":"<p><strong>Objective: </strong>To quantitatively evaluate the bibliographic reliability of AI-generated medical references across multiple chatbot platforms using the Reference Hallucination Score (RHS) and to examine the influence of output format on reference stability.</p><p><strong>Methods: </strong>In this cross-sectional comparative study, three AI-based chatbots (ChatGPT, Gemini, and Perplexity) were prompted under 30 predefined medical subheadings in two output formats (letter and review article), generating 3,150 references. Reference reliability was assessed using the RHS, which evaluates presence/verifiability, bibliographic accuracy, PMID validity, and topic relevance. Due to non-normal distribution, data were analyzed using Kruskal-Wallis and Mann-Whitney U tests with Bonferroni correction. Effect sizes (η² and r) were calculated to distinguish statistical detectability from practical magnitude.</p><p><strong>Results: </strong>Significant inter-model differences were detected in total RHS scores (H(2) = 24.88, p < 0.001); however, the overall effect size was very small (η² = 0.007). Pairwise comparisons revealed statistically detectable differences between certain models, although effect sizes were consistently small (r = 0.02-0.20). Format-related differences were observed, with longer outputs demonstrating reduced bibliographic stability; however, these differences were numerically modest and partially attenuated after Bonferroni correction.</p><p><strong>Conclusion: </strong>AI-based chatbots exhibit measurable bibliographic instability in reference generation, with statistically detectable but practically small differences between models. Beyond cross-platform comparison, this study proposes an instrument-agnostic statistical framework for large-sample evaluation of AI-generated reference reliability. Positioned within applied methodological research rather than AI benchmarking, the framework may serve as a template for future large-scale AI reliability studies. The proposed framework may contribute to methodological standardization in future evaluations of AI-generated scientific outputs.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13523266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148276023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Widening participation in cognitive neuroscience: a mixed-methods study of age-related motivators, barriers and attitudes towards imaging methods.","authors":"Ksenia Kotiusheva, Laurel Hilliker, Tracy Ibbotson, Satu Baylan, Maria Gardani, Gemma Learmonth","doi":"10.1186/s12874-026-02911-3","DOIUrl":"10.1186/s12874-026-02911-3","url":null,"abstract":"<p><strong>Background: </strong>Cognitive neuroscience research often relies on convenience sampling of participants, which can result in biased sample demographics and an under-representation of older adults. There is a need to identify more effective routes to widen participation among older adults and to explore age-related differences in the motivators and barriers to research involvement.</p><p><strong>Methods: </strong>This mixed methods study combined qualitative data from two focus groups, conducted with N = 11 healthy older adults aged 55-73, and an online questionnaire completed by N = 336 adults aged 18-88.</p><p><strong>Results: </strong>Analysis of the focus group discussions identified 3 main themes that were most important to older adults: a) The importance of receiving transparent information about the aims, procedures and safety of the study, b) Distinguishing between medical and non-medical research, and c) Contributing to the \"collective good\". The questionnaire echoed that altruism, and the prospect of scientific discovery, are increasingly important motivators with advancing age, whereas financial incentives become less important. Older adults have more free time to participate, are less deterred by the prospect of pain, and express more trust in researchers than younger people. Attitudes towards different imaging methods (MRI, EEG, NIBS and Eye tracking) varied, with fewest negative emotions for eye-tracking and most for non-invasive brain stimulation, but positive attitudes generally increased and negative attitudes reduced with age.</p><p><strong>Conclusion: </strong>These findings can inform age-tailored recruitment strategies to improve diversity in neuroimaging research. Improving communication, addressing practical barriers, and framing studies in a meaningful context may help increase participation among groups who are traditionally underrepresented in neuroimaging research.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13523283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148283102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triage effectiveness: a framework for quantifying the effect of emergency triage prioritization.","authors":"André Johansson, Viktoria Fodor, Jakob Lundager Forberg, Anna Ekwall, Ulf Ekelund","doi":"10.1186/s12874-026-02918-w","DOIUrl":"10.1186/s12874-026-02918-w","url":null,"abstract":"<p><strong>Background: </strong>Emergency departments use triage to identify time-critical patients and reduce waiting times by assigning higher priority. However, no existing method directly measures this core clinical function. We developed and validated Triage Effectiveness (TE) as a framework for quantifying how well triage systems reduce waiting times for time-critical patients.</p><p><strong>Methods: </strong>Using data from 463,209 visits across eight emergency departments, we developed TE as a scale where 0% equals a first-come-first-serve strategy (no triage) and 100% equals perfect prioritization. The framework includes two complementary measures: 1. Waiting Time-based TE (WTE) measuring actual waiting time reduction, where 100% represents zero waiting time, and 2. Rank-based TE (RTE) measuring queue position improvement where 100% represents placement first in queue. Both can be calculated as theoretical TE (based only on priorities) and observed TE (actual clinical performance). We validated WTE and RTE calculations using analytical queueing models adapted for classification uncertainty and assessed transferability across hospitals.</p><p><strong>Results: </strong>Queueing theory validation showed strong alignment with both WTE and RTE calculations, with RTE demonstrating greater robustness and less sensitivity to ED utilization patterns. TE increased with accuracy and produced negative values when triage performance was worse than chance. When applying the framework we found substantial gaps between theoretical and observed TE across all emergency departments, indicating significant post triage reprioritization.</p><p><strong>Conclusions: </strong>The TE framework provides the first method for directly measuring the effectiveness of triage while utilizing full ordinal priority information. TE can assess the effectiveness of initial triage and the impact of post-triage re-prioritization.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13282852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148283127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}