BMC Medical Research Methodology最新文献

{"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}

{"title":"Tree-based exploratory identification of predictive biomarkers in non-randomized data.","authors":"Julia Krzykalla, Maral Saadati, Wiebke Hielscher, Annette Kopp-Schneider, Hartmut Döhner, Axel Benner, Dominic Edelmann","doi":"10.1186/s12874-026-02928-8","DOIUrl":"10.1186/s12874-026-02928-8","url":null,"abstract":"<p><p>The idea of \"stratified medicine\" is an important driver of methodological research on the identification of predictive biomarkers. Most methods proposed so far for this purpose have been developed for the use on randomized data only. However, often data from clinical registries or non-randomized studies are the only available data source. For such data, methods for estimation of the average treatment effect are well established. Research on confounder adjustment when investigating the heterogeneity of treatment effects and the variables responsible for this is usually restricted to regression modelling. In this paper, we demonstrate how the predMOB, a tree-based method that specifically searches for predictive factors, can be combined with common strategies for confounder adjustment. In an extensive simulation study, we show that the proposed strategies allow for the correct identification of predictive factors in the presence of confounding. It is demonstrated that the most robust method is a combination of both covariate adjustment and Inverse Probability of Treatment Weighting (IPTW). Applications to the German Breast Cancer Study Group (GBSG) trial 2 and the Acute Myeloid Leukemia Study Group (AMLSG) 16-10 study illustrate these conclusions.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13312558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148338088","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":"Comparative evaluation of interrupted time series analytical methods for healthcare quality improvement research: a Monte Carlo simulation study.","authors":"Seungman Kim, Jaehoon Lee","doi":"10.1186/s12874-026-02924-y","DOIUrl":"https://doi.org/10.1186/s12874-026-02924-y","url":null,"abstract":"<p><strong>Background: </strong>Interrupted Time Series (ITS) analysis is increasingly used to evaluate healthcare quality improvement (QI) interventions, yet limited guidance exists on which analytical approach performs best under the complex data conditions typical of QI research.</p><p><strong>Methods: </strong>A Monte Carlo simulation study was conducted using a full factorial design with 512 unique scenarios (varying time series length, autocorrelation, trend type, seasonality, noise level, intervention effects, external shocks, and covariates), each replicated 500 times, yielding 256,000 simulated datasets. Four models were compared: ARIMAX, Generalized Additive Models (GAM), Prophet, and Long Short-Term Memory (LSTM) networks. Model performance was evaluated using bias, root mean squared error (RMSE), statistical power, and Type I error rate. For counterfactual-based models (Prophet and LSTM) lacking traditional p-values, an empirical null distribution approach was used to derive comparable significance tests.</p><p><strong>Results: </strong>ARIMAX demonstrated the highest estimation accuracy (RMSE = 7.85) and statistical power for detecting level changes (72.3%), followed by GAM (RMSE = 8.69; power = 70.2%). However, both parametric models exhibited inflated Type I error rates (ARIMAX: 13.8%; GAM: 9.1% for level change), particularly pronounced for slope change detection (ARIMAX: 37.2%). In contrast, Prophet maintained well-controlled Type I error (4.8%) but with substantially lower power (15.4%) and higher estimation error (RMSE = 20.1). LSTM achieved 25.4% power and an RMSE of 14.8 among the 35.5% of simulated datasets in which the model converged; LSTM failed to converge in 64.5% of simulated datasets, predominantly in short series (T = 36 months). Seasonality was the dominant source of estimation bias for parametric models, while trend type was the primary driver for counterfactual models. Model performance degraded progressively with scenario complexity, with power declining from near 100% (simplest conditions) to approximately 50% (most complex conditions) for ARIMAX and GAM.</p><p><strong>Conclusions: </strong>No single method dominates across all performance criteria. ARIMAX offers the best accuracy and power but risks false positives, particularly for slope change effects. GAM provides a balanced alternative with better Type I error control and robustness to nonlinear trends. Prophet and LSTM are conservative approaches suitable when avoiding false positive conclusions is paramount. These findings provide empirical guidance for selecting ITS analytical methods appropriate to specific QI research contexts and priorities.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148326943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological advances in claims-based dementia algorithms: integrating medication and clinical data for medicare populations.","authors":"Sara Knox, Janet Horn, Kit Simpson, Bethany Wolf","doi":"10.1186/s12874-026-02929-7","DOIUrl":"10.1186/s12874-026-02929-7","url":null,"abstract":"<p><strong>Background: </strong>Accurate identification of Alzheimer's Disease and Related Dementias (ADRD) in Medicare data is essential for research, policy, and care planning. Existing approaches, including the Centers for Medicare & Medicaid Services (CMS) Chronic Conditions Warehouse (CCW) algorithm, rely primarily on International Classification of Diseases, Tenth Revision (ICD-10) codes and are subject to misclassification and potential bias across populations.</p><p><strong>Methods: </strong>We evaluated whether incorporating neuropsychiatric symptom (NPS) ICD-10 codes and medication data improves prediction of algorithm-defined ADRD classificaitons. We analyzed 269,214 Medicare beneficiaries receiving home health services in 2019 using claims, assessment, and prescription drug files. Two classification algorithms were examined: (1) the CCW binary classification algorithm and (2) a clinically informed ADRD ternary classification algorithm (ADRD-highly likely, ADRD-possible, ADRD-unlikely). Predictive models were developed for each algorithm using stacked elastic net regression, incorporating demographics, healthcare utilization, psychiatric and cognitive indicators, and medication use. Models were trained on beneficiaries with definitive ADRD status and evaluated in an independent test set using area under the curve (AUC), sensitivity, specificity, and predictive values. Thresholds were selected to prioritize specificity (≥ 80%) while maximizing sensitivity.</p><p><strong>Results: </strong>In the test dataset, the CCW predictive model achieved an AUC of 0.863 for predicting CCW binary-defined ADRD status. The ADRD predictive model achieved a higher AUC of 0.906 when predicting ADRD ternary-defined classifications. Both models demonstrated strong discrimination for their respective outcomes, but performance characteristics differed. Among beneficiaries classified as ADRD-possible, the ADRD predictive model more frequently classified individuals as ADRD-negative, indicating a more conservative approach to ambiguous cases.</p><p><strong>Conclusions: </strong>Incorporating NPS codes and medication data improves prediction of algorithm-defined ADRD classifications, particularly among individuals with uncertain status. Models aligned with the ADRD ternary algorithm demonstrated more specific and conservative classification patterns, suggesting that integrating behavioral, cognitive, and medication data may enhance the validity of claims-based phenotyping. Further validations against clinical diagnosis are needed.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13573462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148326905","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":"An interpretable XGboost algorithm for predicting 30-day mortality in acute pancreatitis using routine biomarkers.","authors":"Jun Zhou, Ying Chen, Jingping Liu, Wenyang Hou, Bin Pei, Mengxiao Xie","doi":"10.1186/s12874-026-02900-6","DOIUrl":"10.1186/s12874-026-02900-6","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate a machine learning (ML) algorithm for predicting 30-day mortality in adult patients with acute pancreatitis (AP) using routinely readily available laboratory parameters.</p><p><strong>Methods: </strong>This retrospective cohort study analyzed 965 consecutive AP patients hospitalized between January 2017 and December 2019. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by the evaluation of eleven machine learning algorithms. Model performance was assessed using via the area under the receiver operating characteristic curve (AUC). The optimal model was further interpreted using SHapley additive explanation (SHAP).</p><p><strong>Results: </strong>The overall 30-day mortality rate was 4.5% (43/965). Among the evaluated algorithms, extreme gradient boosting (XGboost) demonstrated the best performance in both the development and validation cohorts. SHAP analysis identified creatine kinase (CK) as the most influential predictor, followed by lactate dehydrogenase (LDH), age, prothrombin time (PT), and carbohydrate antigen 19 - 9 (CA19-9). A simplified algorithm incorporating only the top five features achieved near-perfect discrimination in the development cohort (AUC = 1.000) and maintained robust performance in the validation cohort (AUC = 0.847). This five-feature XGboost algorithm showed superior performance compared to conventional prognostic scores in the development cohort.</p><p><strong>Conclusion: </strong>An interpretable XGboost algorithm incorporating five key parameters (CK, LDH, age, PT, CA19-9) shows potential to predict predicts 30-day mortality in AP patients. This single-center retrospective model demonstrates promising internal performance but requires prospective multicenter external validation before clinical implementation. The web-based platform is provided as a research prototype for proof-of-concept and must not be used for real-time clinical decision-making at this stage.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13560264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148326872","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":"Increasing power and robustness in screening trials by testing stored specimens in the control arm.","authors":"Hormuzd A Katki, Li C Cheung","doi":"10.1186/s12874-026-02920-2","DOIUrl":"10.1186/s12874-026-02920-2","url":null,"abstract":"<p><strong>Background: </strong>Screening trials require large sample sizes and long time-horizons to demonstrate mortality reductions. We recently proposed increasing statistical power by testing stored control-arm specimens, called the \"Intended Effect\" (IE) design. The IE design increases power by increasing the effect size via restricting analysis to only those people in both arms who are affected by screening, namely, those who test-positive. Hence the design requires storing control-arm specimens for future testing. To evaluate feasibility of the IE design, the US National Cancer Institute (NCI) is collecting blood specimens in the control-arm of the NCI Vanguard Multicancer Detection pilot feasibility trial. However, key assumptions of the IE design require more investigation and relaxation.</p><p><strong>Methods: </strong>We relax the IE design to (1) reduce costs by testing only a stratified sample of control-arm specimens by incorporating inverse-probability sampling weights, (2) correct for potential loss-of-signal in stored control-arm specimens, and (3) correct for non-compliance with control-arm specimen collections. We also examine sensitivity to \"unintended effects\" of screening (i.e. outcome rates in screen-negatives increasing due to false reassurance).</p><p><strong>Results: </strong>In simulations, testing all primary-outcome control-arm specimens and a 50% sample of the rest maintains nearly all the power of the IE while only testing half the control-arm specimens. Power usually remains increased from the IE analysis (versus the standard analysis) even if unintended effects exist. The IE design is robust to some loss-of-signal scenarios, but otherwise requires retest-positive fractions that correct bias at a small loss of power. The IE can be biased and lose power under control-arm non-compliance scenarios, but corrections correct bias and can increase power.</p><p><strong>Conclusions: </strong>The IE design can be made more cost-efficient and robust to loss-of-signal. Unintended effects will not typically reduce the power gain over the standard trial design. Non-compliance with control-arm specimen collections can cause bias and loss of power that can be mitigated by corrections. Although promising, practical experience with the IE design in screening trials is necessary.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148315390","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":"Optimising data completeness and quality in multicentre randomised clinical trials using central data monitoring: a pragmatic approach.","authors":"Markus Harboe Olsen, Mathias Lühr Hansen, Janus Engstrøm, Sanam Safi-Rasmussen, Gorm Greisen, Christian Gluud","doi":"10.1186/s12874-026-02932-y","DOIUrl":"10.1186/s12874-026-02932-y","url":null,"abstract":"<p><strong>Background: </strong>Randomised clinical trial data monitoring is essential for ensuring quality and adherence to protocols, often achieved through on-site methods. However, challenges like the COVID-19 pandemic have highlighted limitations. Central data monitoring offers an alternative, leveraging digital capabilities and diverse expertise.</p><p><strong>Methods: </strong>The SafeBoosC-III trial, randomising 1,601 preterm infants across 70 sites, was a pragmatic trial offering detailed education of the investigators before launch. During the trial, we conducted pre-protocolised central data monitoring encompassing monthly data checks, data completeness assessments, and statistical analyses. Discrepancies were primarily identified through visual interpretation of data.</p><p><strong>Results: </strong>Throughout the trial, meticulous central monitoring helped keeping missing data at only 1.4%. Of 43,965 data points reviewed, 338 were flagged, with 81 (0.2% of all data and 24.0% of flagged data) corrected after investigation.</p><p><strong>Conclusions: </strong>The methodology of combining manual investigation with statistical tools effectively minimised data errors, enhancing validity. This approach in the SafeBoosC-III trial emphasises the importance of ongoing central data monitoring in clinical trials. Standardised protocols for such monitoring are crucial for ensuring data quality and accelerating the validation process in future trials.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148315392","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}