BMC Medical Research Methodology最新文献

{"title":"Digital focus groups in healthcare interventions: A reflexive methodological analysis of how socio-technical and relational factors co-produce qualitative data.","authors":"Jan Gehrmann, Johannes Stephan, Ananda Stullich, Julia Roick, Laura Hoffmann, Matthias Richter","doi":"10.1186/s12874-026-02945-7","DOIUrl":"10.1186/s12874-026-02945-7","url":null,"abstract":"<p><strong>Background: </strong>Digital focus groups are increasingly used in health research, yet their methodological implications within complex interventions remain insufficiently examined. When conducted with pre-existing cohorts, digital focus groups may function not only as evaluative tools but also as relational and socio-technical encounters that shape the generation of qualitative data. The goal of the present study was to examine how digital focus groups operate within this context.</p><p><strong>Methods: </strong>This study presents a methodological re-analysis of focus group material originally collected for the evaluation of a hybrid psychosocial prevention intervention, examining how digital focus groups operate within this context following the perspective of Situational Analysis. Digital focus groups were conducted with participants who had completed the hybrid intervention together in fixed cohort groups, meaning participants were already acquainted with one another prior to the focus group sessions. For the analysis, the transcripts were examined using descriptive, inductive coding. Verbatim quotations were used as methodological markers to illustrate how relational, emotional and technological factors shaped the data.</p><p><strong>Results: </strong>Pre-existing cohort cohesion strongly influenced how participants engaged in the digital setting, fostering emotional safety, peer-supported learning, motivational reinforcement and opportunities for self-regulation. Digital platforms structured visibility, turn-taking and synchronization, while differences in devices, connectivity and digital literacy affected participation and the stability of group interaction. The researcher role expanded to include facilitation, technical troubleshooting and emotional anchoring, making the research team part of the socio-technical infrastructure. At times, the focus groups elicited supportive or motivational effects that echoed elements of the intervention itself, blurring the line between evaluation and unintended interventional effects.</p><p><strong>Conclusions: </strong>Focus groups in intervention research operate as relational encounters rather than neutral data-collection tools. This applies particularly to Digital Focus Groups. Recognizing how pre-existing relationships, technological environments and researcher involvement shape interaction is essential for methodological transparency and rigorous interpretation. The findings highlight the importance of reflexivity, attention to digital differences, and awareness of unintended interventional dynamics when designing and interpreting digitally mediated qualitative methods in healthcare contexts. Furthermore, the findings indicate that the choice between digital and in-person FGs should not be guided by pragmatic considerations alone, but by the relational, technological and institutional conditions of the research situation.</p><p><strong>Trial registration: </strong>DRKS-ID: DRKS000330","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13356787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418742","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":"Large language models vs. conventional search in identifying delirium guidelines for a scoping review.","authors":"Mary Faherty, Ronan Madden, Breeda Herlihy, Siobhan Bowman, Alasdair MacLullich, Suzanne Timmons","doi":"10.1186/s12874-026-02941-x","DOIUrl":"10.1186/s12874-026-02941-x","url":null,"abstract":"<p><strong>Background: </strong>A literature search of information sources is a key methodological step for evidence synthesis, including scoping reviews. This study compares the performance of large language models with conventional systematic literature search methods in identifying published delirium clinical practice guidelines.</p><p><strong>Methods: </strong>Comparative study using parallel searches. Four large language models (ChatGPT, Google Gemini, Claude and Grok) were queried using structured prompts, with results assessed for precision, sensitivity, and efficiency, versus a conventional systematic literature search using established bibliographic databases and grey literature sources.</p><p><strong>Results: </strong>Large language models sourced 17 of the 45 guidelines identified through a comparable conventional search, missing 28, but also identified two guidelines not found through the conventional search. The sensitivity of individual large language models ranged from 9% to 29%, using the conventional search as a reference standard. Of the 19 guidelines retrieved through large language models, 63% were deemed high quality, compared with 56% identified through the conventional search. Precision of individual large language models varied from 22% to 60%, far exceeding the precision of the conventional search (1.7%). Large language model-based searches required substantially less execution time compared to conventional methods.</p><p><strong>Conclusions: </strong>While large language models have a significantly lower yield in retrieving clinical guidelines compared to the conventional search, their efficiency, along with their capacity to identify some unique articles, suggests a possible role in complementing conventional search methods in scoping reviews.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418727","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":"Single-label and multi-label classification for disease recognition with special consideration of comorbidities.","authors":"Sophie Schmiegel, Hannah Marchi, Marvin-Hendrik Röchter, Martin Rudwaleit, Christiane Fuchs","doi":"10.1186/s12874-026-02934-w","DOIUrl":"10.1186/s12874-026-02934-w","url":null,"abstract":"<p><strong>Background: </strong>Certain diseases require rapid treatment to avoid long-term consequences for patients. However, they may be difficult to recognize, especially if the symptoms are ambiguous and compatible with multiple possible diagnoses. Completing all necessary examinations often takes time, thereby prolonging patient suffering. Data-driven approaches, such as single-label classification (SLC) and multi-label classification (MLC), can help accelerate the diagnostic process and improve accuracy.</p><p><strong>Methods: </strong>Comparing SLC and MLC allows us to investigate whether disease recognition benefits from considering comorbidities. In this context, we aim to provide a conceptual framing of (differences between) the two approaches in model formulation, decision spaces and handling of class imbalance. To empirically assess their performance, we conduct a case study applying SLC and MLC to data from chronic pain patients.</p><p><strong>Results: </strong>Our analysis yields an ambiguous picture of whether incorporating comorbidities improves disease recognition. The suitability of SLC and MLC is determined by multiple factors, notably the dependency structure among diseases and between diseases and covariates, as well as by data characteristics such as class imbalance. Consequently, it is of high importance to address each individual problem in an individual manner, especially when the implications can be significant, such as in patient referrals.</p><p><strong>Conclusion: </strong>Our study highlights the importance of considering the specific characteristics of the data when selecting an appropriate classification approach for disease recognition and beyond. We illustrate how underlying assumptions, learning structures, and strategies for handling class imbalance shape predictive performance. Our results thus lead to the point that computational prediction tools should serve as decision support for treating physicians, but never replace their clinical judgment.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13355337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418771","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":"Bayesian inference of interval-censored data with an application to HIV population surveys: a simulation study comparing Hamiltonian Monte Carlo and Metropolis-Hastings sampling algorithms.","authors":"Alexander van Twisk, Innocent Maposa","doi":"10.1186/s12874-026-02922-0","DOIUrl":"10.1186/s12874-026-02922-0","url":null,"abstract":"<p><strong>Background: </strong>HIV incidence estimation in population-based surveys often relies on interval-censored seroconversion times and complex survey designs, requiring computationally efficient Bayesian methods. We compared the computational efficiency and inferential performance of Hamiltonian Monte Carlo (HMC) and Metropolis-Hastings (MH) for Bayesian analysis of interval-censored HIV seroconversion times using a weighted log-logistic accelerated failure-time model.</p><p><strong>Methods: </strong>We conducted a simulation study of 5,400 datasets varying sample size, censoring, and weight dispersion under identical likelihoods, priors, diagnostics, and convergence criteria for both samplers, and applied the same model to the Zimbabwe PHIA 2020 survey (ZIMPHIA). Performance was assessed using efficiency (effective sample size per second, ESS/s), accuracy, interval calibration, and standard convergence diagnostics.</p><p><strong>Results: </strong>HMC delivered substantially higher sampling efficiency across scenarios while producing comparable point estimates, uncertainty, and coverage. On ZIMPHIA ([Formula: see text]), HMC delivered [Formula: see text] higher effective sample size per second than MH for [Formula: see text], equivalent to 1.43 vs 31.83 minutes of wall time at matched effective sample sizes.</p><p><strong>Conclusions: </strong>HMC is a practical default for weighted, interval-censored survival analysis in HIV surveys, with benefits that increase with sample size and weight variability.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418753","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":"Leveraging generative artificial intelligence for the development of non-interventional research study protocols: a proof-of-concept feasibility study.","authors":"Julia Langham, Emma Benbow, Tim Reason, Bill Malcolm, Andy Gimblett, Cheryl Jones, Nathan R Hill","doi":"10.1186/s12874-026-02937-7","DOIUrl":"10.1186/s12874-026-02937-7","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The writing of study protocols is a labor and time-intensive process. We hypothesized that the writing of some methodological aspects of study protocols for non-interventional studies (NIS) could be amenable to automation using generative artificial intelligence (GenAI), particularly when provided with standardized templates, and a set of common elements, such as study design and objectives.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this proof-of-concept feasibility study, we explored whether a large language model (LLM), specifically GPT-4, could support the drafting of selected methodological sections of a study protocol for retrospective observational NIS. The sections to be populated were the objectives, study design, study population, data source, data collection methods, statistical analysis methods and study strengths and limitations. A Python application programming interface (API) was used to send instructions, \"prompts\", to GPT-4 including guideline instructions, examples of text and specific study inputs related to the research question, including study population, objectives, and data source/data collection methods. The LLM-based program was first tested using four case studies. The program was then revised and refined using an additional four previously unseen case studies, to explore whether the prompting framework could be applied across different objectives, populations and datasets within the defined scope of retrospective observational NIS (quality assessment set, wave 1). Finally, the revised program was applied to a further five previously unseen protocols as a final feasibility assessment (quality assessment set, wave 2). A critical appraisal of the nine protocols populated by GPT-4 in the quality assessment stages was conducted to explore the alignment of the GPT-4 text in relation to the original human-written protocols and against standard guidelines. In addition, two GPT-4 written protocols were blind-reviewed (i.e. \"author\" unknown) through the Company's routine internal scientific review process.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The critical appraisal of the nine GPT-4-produced protocols from the quality assessment sets suggested that the GPT-4 text aligned well with both the original human produced content and with content required by guidelines. This was further demonstrated by the two blind-reviewed protocols which were approved with only minor comments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Considerable effort and time (~ 3-6 months) were required to develop the structured prompt engineering workflow with prompts that were transferable to other NIS protocols. When provided with integral details to specify the elements of the study question (via human input), GPT-4 showed promising alignment with human-authored reference protocols under a constrained use case. Critical human input remains essential to define the study question, provide structured inputs, and review and revise the generated text ","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13628891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148403629","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":"The dilemma of using racial and ethnic categories in biomedical research.","authors":"A S Kidane Gebremeskel, K A C Meeks, M H Cnossen, A M'charek, A W Rijneveld","doi":"10.1186/s12874-026-02917-x","DOIUrl":"10.1186/s12874-026-02917-x","url":null,"abstract":"<p><p>Ethno-racial categories (ERCs) are inconsistently operationalized in biomedical research, with challenges in incorporating multiracial subjects and reliance on proxies like country of birth. We emphasize the need for transparency in ERC data collection and propose peer reviewer guidelines to standardize and critically evaluate ERC usage in studies.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13348466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410161","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":"Variable selection for clinical prediction models in low-dimensional data - a simulation study comparing traditional regression and machine learning methods.","authors":"Johannes A Vey, Georg Heinze, Meinhard Kieser","doi":"10.1186/s12874-026-02930-0","DOIUrl":"10.1186/s12874-026-02930-0","url":null,"abstract":"<p><strong>Purpose: </strong>A wide range of methods exist for developing a clinical prediction model (CPM) and for performing variable selection. Our purpose was to develop a fair simulation study design and to investigate the properties, strengths, and weaknesses of different methods to predict a continuous outcome in low-dimensional data situations.</p><p><strong>Methods: </strong>In this simulation study, we conducted a neutral comparison of traditional (linear regression with stepwise selection) and machine learning (regularized regression with elastic net, gradient boosting, random forest) variable selection strategies to derive a CPM. The generated datasets included a total of 15 variables, with 8 of those being predictor variables. Four data- and outcome-generating mechanisms with increasing complexity produced data structures typical for biomedicine covering linear associations and gradually introducing non-linear and non-additive elements into the data structure.</p><p><strong>Results: </strong>All methods generally performed better with increasing sample size and less noise in the data. Gradient boosting with regression models and with trees as base learners, and the elastic net regularized regression included nearly all variables (i.e., both the predictor and non-predictor variables), especially with increasing sample size. The linear regression model with stepwise selection (LMSS) showed the best trade-off between correctly including the predictors and excluding the non-predictor variables in most of the scenarios, even when the functional form of continuous predictors deviated from linearity. In more complex data, variable selection using the Boruta or Hapfelmeier approach for random forest performed similar to LMSS.</p><p><strong>Conclusion: </strong>The sample size must be sufficiently large to enable the methods to reliably identify the predictor variables and to ensure that the developed CPMs are accurate and well-calibrated. LMSS revealed good properties and the random forest with the Boruta or Hapfelmeier approach are suitable alternatives if complex associations between predictors and outcomes are assumed.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"26 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13340120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148403632","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":"Integrating health economics and implementation science: a call to action.","authors":"Lane Meredith, Helen Skouteris, Sarah Alderson, Mamta Bajre, Ruth E Barker, Paul Bird, Caroline S Clarke, Graeme Currie, Robbie Foy, Samuel Frempong, Clare Glover-Wright, Robert Heggie, Agnieszka Latuszynska, Hugh McLeod, Seyran Naghdi, Siyabonga Ndwandwe, Emily Rowe, Carlos Sillero-Rejon, Sophie Staniszewska, Michael Sykes, Davide Tebaldi, Mehdi Yousefi, Mandana Zanganeh, Amy L Grove, Alisa M Higgins","doi":"10.1186/s12874-026-02933-x","DOIUrl":"10.1186/s12874-026-02933-x","url":null,"abstract":"<p><strong>Background: </strong>Health economics and implementation science play a critical role in the uptake of evidence-based practice but have largely sat siloed.</p><p><strong>Methods: </strong>This paper summarises findings from a 3-day workshop on health economics and implementation science. Workshop attendees included 30 health economists, implementation scientists, patient contributors, and patient and public involvement and engagement researchers from Australia and the United Kingdom. A shared vision for moving from siloed to synergistic disciplinary approaches was derived through consensus.</p><p><strong>Results: </strong>This article outlines to researchers and methodologists what synergistic disciplinary approaches could look like. We highlight opportunities for health economics and implementation science to integrate along the innovation pathway, from the development and evaluation of innovation to eventual uptake and spread.</p><p><strong>Conclusions: </strong>Greater collaboration between implementation scientists and health economists has the potential to optimise implementation strategies, provide robust evidence for value for money and ultimately improve care delivery. Stronger integration of health economics and implementation science may also shed more light on the equity impacts of implementation strategies and guide their further design to promote more equitable care and outcomes.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13466652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387181","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":"Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.","authors":"John Tazare, Jeremy P Brown, Daniel R Morales, Liam Smeeth, Stephen J W Evans, Ian J Douglas, Elizabeth J Williamson","doi":"10.1186/s12874-026-02926-w","DOIUrl":"10.1186/s12874-026-02926-w","url":null,"abstract":"<p><strong>Background: </strong>Growing availability of laboratory test result information in large healthcare databases presents opportunities to improve confounding control. Whilst information on the ordering of tests and the resulting continuous test values are available, their optimal integration into data-driven confounding control strategies like the high-dimensional propensity score (HDPS) remains unclear.</p><p><strong>Methods: </strong>We propose methods to incorporate test-related data into HDPS, addressing key concerns around data quality and missing data. We illustrate these approaches using UK Clinical Practice Research Datalink GOLD data comparing COPD-specific mortality in new-users of proton pump inhibitors (PPIs) and H2-receptor antagonists (H2RAs). Hazard ratios (HRs) were estimated using Cox models weighted by inverse HDPS. Integration of 35 blood-test values was achieved via biologically informed cut-offs and using a missing-indicator approach for continuous data. Results were benchmarked against a HDPS model derived from clinical, referral, and prescription data dimensions.</p><p><strong>Results: </strong>Among 733,885 new PPI and 124,410 H2RA users, the adjusted HR for PPI use and COPD mortality was 1.36 (95% CI: 1.14-1.64) in the primary HDPS analysis. Incorporating test-requested data and continuous blood test values attenuated estimates towards the expected null association (HR 1.24; 95% CI: 1.02-1.56). Of the 500 HDPS covariates selected in the final model, 46% were derived from test-related data.</p><p><strong>Conclusion: </strong>We empirically evaluated methods for incorporating laboratory test results into the HDPS framework, demonstrating their potential to reduce residual confounding in UK EHR studies. We propose principled approaches for incorporating laboratory test result data into data-driven confounding control strategies. Results from our case study highlight the potential for these data to reduce residual confounding in UK EHR studies. When available, test information should be considered within HDPS.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13584297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148359200","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":"Sparse multi-way DMDC for longitudinal classification in high dimension low sample size data.","authors":"Zahra Zolghadr, Hamid Alavi Majd, Mohammad Rouzbeh, Yadollah Mehrabi, Amir Eftekhari Moghadam, Salman Safdari","doi":"10.1186/s12874-026-02810-7","DOIUrl":"10.1186/s12874-026-02810-7","url":null,"abstract":"<p><p>Recent advances in data registration techniques have enabled researchers to analyze high-dimensional, low sample size (HDLSS) data, which poses challenges for traditional statistical and machine learning methods-especially in fields like neurodevelopmental disorder classification. Among various proposed solutions, the Data Maximum Dispersion Classifier (DMDC) has demonstrated superior performance. This study introduces two extensions: Sparse DMDC (SDMDC) and Longitudinal Sparse DMDC (LSDMDC), with the latter leveraging multi-way data frameworks. Simulated HDLSS datasets were generated from multivariate normal distributions with sparse structures. Evaluation of SDMDC involved varying class imbalance, predictor count, and correlation structures. Performance was measured via average F-measure and signal-to-noise ratio (SNR). Results showed that SDMDC outperformed other sparse models. Moreover, promising improvement was observed in model classification performance and the selection of effective predictors when the correlation between predictor variables was increased. In longitudinal simulations, LSDMDC maintained performance even as the number of predictors increased along the first dimension, effectively handling rank-1 and rank-2 data. Notably, LSDMDC achieved the best results under autoregressive correlation. In conclusion, SDMDC demonstrated clear advantages over other sparse classifiers, while LSDMDC proved highly effective for complex longitudinal structures.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148350702","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}