BMC Medical Research Methodology最新文献

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The impact of randomization techniques on the performance of pre-post design models. 随机化技术对前后设计模型性能的影响。
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-19 DOI: 10.1186/s12874-025-02632-z
Xinlin Lu, Yahui Zhang, Samuel S Wu, Guogen Shan
{"title":"The impact of randomization techniques on the performance of pre-post design models.","authors":"Xinlin Lu, Yahui Zhang, Samuel S Wu, Guogen Shan","doi":"10.1186/s12874-025-02632-z","DOIUrl":"10.1186/s12874-025-02632-z","url":null,"abstract":"<p><p>Pre-post designs are widely used in clinical trials and experimental studies to assess the effectiveness of treatments. Common statistical methods for analyzing pre-post data include analysis of variance (ANOVA) using post-treatment or the change from baseline, analysis of covariance (ANCOVA) with homogeneous or heterogeneous slopes, and linear mixed models (LMM). While numerous studies have compared these methods, limited studies have investigated the impact of adjusting for influential baseline covariates under different randomization approaches. In this study, we conducted a series of comprehensive simulation studies to investigate the impact of adjusting baseline covariates under several randomization approaches: simple randomization, stratified block randomization, and covariate adaptive randomization using the minimization method by Pocock and Simon. Results demonstrated that when no covariates were considered in the randomization approach, the two ANCOVA methods always have good performance. Adjusting for relevant baseline covariates led to substantial power gains, with the extent of these gains depending on the size of the covariate effects and the randomization approach employed. Stratified block randomization and covariate adaptive randomization consistently outperformed simple randomization in terms of power gains after adjusting for covariates, with covariate adaptive randomization becoming more superior as the number of covariates increased.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"176"},"PeriodicalIF":3.9,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12276662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144667044","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}
引用次数: 0
Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data. 对阿尔茨海默病动态预测的思考:纵向结果和事件时间数据建模的进展
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-17 DOI: 10.1186/s12874-025-02618-x
Durong Chen, Meiling Zhang, Hongjuan Han, Yalu Wen, Hongmei Yu
{"title":"Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.","authors":"Durong Chen, Meiling Zhang, Hongjuan Han, Yalu Wen, Hongmei Yu","doi":"10.1186/s12874-025-02618-x","DOIUrl":"10.1186/s12874-025-02618-x","url":null,"abstract":"<p><strong>Background: </strong>Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer's disease (AD), encompassing both conventional statistical methods and deep learning techniques.</p><p><strong>Methods: </strong>Articles were sourced from PubMed, Embase and Web of Science databases using keywords related to dynamic prediction of AD. A set of criteria was developed to identify included studies. The correlation information for the construction of models was extracted.</p><p><strong>Results: </strong>We identified four methodological frameworks for dynamic prediction from 18 studies with two-stage model (n = 3), joint model (n = 11), landmark model (n = 2) and deep learning (n = 2). We reported and summarized the specific construction of models and their applications.</p><p><strong>Conclusions: </strong>Each framework possesses distinctive principles and attendant benefits. The dynamic prediction models excel in predicting the prognosis of individual patients in a real-time manner, surpassing the limitations of traditional baseline-only prediction models. Future work should consider various data types, complex longitudinal data, missing data, assumption violations, survival outcomes, and interpretability of models.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"175"},"PeriodicalIF":3.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658321","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}
引用次数: 0
Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study. 使用机器学习表征调查未完成的个人和方法学风险因素:来自美国千年队列研究的发现。
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-14 DOI: 10.1186/s12874-025-02620-3
Nate C Carnes, Claire A Kolaja, Crystal L Lewis, Sheila F Castañeda, Rudolph P Rull
{"title":"Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study.","authors":"Nate C Carnes, Claire A Kolaja, Crystal L Lewis, Sheila F Castañeda, Rudolph P Rull","doi":"10.1186/s12874-025-02620-3","DOIUrl":"10.1186/s12874-025-02620-3","url":null,"abstract":"<p><strong>Background: </strong>Missing survey data can threaten the validity and generalizability of findings from longitudinal cohort studies. Respondent characteristics and survey attributes may contribute to patterns of survey non-completion, a form of missing data in which respondents begin but do not finish a survey, that can lead to biased conclusions. The objectives of the present research are to demonstrate how machine learning can identify survey non-completion and to characterize individual and methodological factors that are associated with this form of data missingness.</p><p><strong>Methods: </strong>The present study developed a novel machine learning algorithm to characterize survey non-completion in the Millennium Cohort Study during the 2019-2021 data collection cycle that included a 30- to 45-min paper or web-based follow-up survey for previously enrolled panels (Panels 1-4, n = 80,986) and a 30- to 45-min web-based baseline survey for new enrollees (Panel 5, n = 58,609). We then examined the effect of individual characteristics and survey attributes on survey non-completion.</p><p><strong>Results: </strong>This algorithm achieved 99% accuracy and showed that 0.29% of follow-up respondents and 15.43% of new enrollees were survey non-completers. Our findings suggest that certain military and sociodemographic characteristics (e.g., enlisted pay grades) were associated with increased survey non-completion in the 2019-2021 cycle. Survey attributes explained a large proportion of the variability in survey non-completion, with our analyses indicating a higher likelihood of survey non-completion in Sects. (1) located toward the beginning of the survey, (2) with sensitive questions, and (3) with fewer questions.</p><p><strong>Conclusion: </strong>This research highlights the importance of accounting for potential respondent bias due to survey non-completion and identifies factors associated with this type of missing data.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"174"},"PeriodicalIF":3.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144636170","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}
引用次数: 0
A framework for clinical care pathway renewal: an example from inflammatory bowel disease. 临床护理途径更新的框架:以炎症性肠病为例。
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-09 DOI: 10.1186/s12874-025-02616-z
K D Chappell, L Olayinka, R Sutton, C H Seow, J deBruyn, S V van Zanten, C Ma, B Halloran, L A Dieleman, K Wong, R Panaccione, K I Kroeker
{"title":"A framework for clinical care pathway renewal: an example from inflammatory bowel disease.","authors":"K D Chappell, L Olayinka, R Sutton, C H Seow, J deBruyn, S V van Zanten, C Ma, B Halloran, L A Dieleman, K Wong, R Panaccione, K I Kroeker","doi":"10.1186/s12874-025-02616-z","DOIUrl":"10.1186/s12874-025-02616-z","url":null,"abstract":"<p><strong>Background: </strong>Clinical care pathways (CCPs) contribute to standardized, high-quality care and reduce variation in healthcare delivery. However, CCPs must be regularly reviewed and updated to reflect current evidence-based clinical practice guidelines. In the literature, the development and implementation of CCPs is well described, but there is little to guide the process for renewal of CCPs as new evidence becomes available.</p><p><strong>Methods: </strong>We sought to develop a recurrent framework for CCP renewal and apply this to provincial established CCPs for inflammatory bowel disease (IBD). The proposed framework was guided by a review matrix stratified based on two factors: risk to change and clinical impact of the CCPs. An in-person CCP workshop was conducted to facilitate advisor engagement, establish consensus on the review process, and apply the review matrix to existing IBD CCPs. Attendees, including gastroenterologists, nurses, pharmacists, colorectal surgeons, and a family physician, were invited to offer feedback on the proposed framework and vote on the definitions associated with each level of the matrix. They then applied the framework to existing CCPs in two voting rounds.</p><p><strong>Results: </strong>A proposed framework for CCP renewal was drafted by a multidisciplinary leadership group and presented to 22 IBD stakeholders at the in-person workshop. After a discussion, attendees agreed the matrix should include four levels, based on either high or low risk to change and high or low clinical impact. Risk to change was defined as how quickly new evidence would evolve and render the CCP out of date, and clinical impact was defined as how important the CCP is to quality IBD care. Using the revised review matrix, the attendees were able to reach agreement regarding the level to be assigned to each existing CCP.</p><p><strong>Conclusions: </strong>The framework, based on risk to change and clinical impact is a valuable starting point to standardize the process of updating and renewing clinical pathways. Revising CCPs using our proposed framework ensures pathways are up-to-date and available to assist healthcare professionals in clinical decision-making. This framework can be adapted and customized to suit CCPs across healthcare disciplines and to facilitate the establishment of a renewal process.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"173"},"PeriodicalIF":3.9,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12239251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144599365","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}
引用次数: 0
CASCADE: a community-engaged action model for generating rapid, patient-engaged decisions in clinical research. CASCADE:一个社区参与的行动模式,用于在临床研究中产生快速的、患者参与的决策。
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-01 DOI: 10.1186/s12874-025-02565-7
Bridgette L Kelleher
{"title":"CASCADE: a community-engaged action model for generating rapid, patient-engaged decisions in clinical research.","authors":"Bridgette L Kelleher","doi":"10.1186/s12874-025-02565-7","DOIUrl":"10.1186/s12874-025-02565-7","url":null,"abstract":"<p><strong>Background: </strong>Integrating patient and community input is essential to the relevance and impact of patient-focused research. However, specific techniques for generating patient and community-informed research decisions remain limited. This manuscript describes a novel CASCADE method (Community-Engaged Approach for Scientific Collaborations and Decisions) that was developed and implemented to make actionable, patient-centered research decisions during a federally funded clinical trial.</p><p><strong>Methods: </strong>The CASCADE method was developed to facilitate decision-making, combining techniques from a variety of past methodologies with new approaches that aligned with project constraints and goals. The final result was a series of procedures that spanned seven thematic pillars (1) identifying a shared, specific, and actionable goal; (2) centering community input; (3) integrating both pre-registered statistical analyses and exploratory \"quests\"; (4) fixed-pace scheduling, supported by technology; (5) minimizing opportunities for cognitive biases typical to group decision making; (6) centering diversity experiences and perspectives, including those of individual patients; (7) making decisions that are community-relevant, rigorous, and feasible. The final approach was piloted within an active clinical trial, with the primary goal of describing feasibility (participation, discussion topics, timing, quantity of outputs).</p><p><strong>Results: </strong>The inaugural CASCADE panel aimed to identify ways to improve an algorithm for matching patients to specific types of telehealth programs within an active, federally funded clinical trial. The panel was attended by 27 participants, including 5 community interest-holders. Data reviewed to generate hypotheses and make decisions included (1) pre-registered statistical analyses, (2) results of 12 \"quests\" that were launched during the panel to answer specific panelist questions via exploratory analyses or literature review, (3) qualitative and quantitative patient input, and (4) team member input, including by staff who represented the focal patient population for the clinical trial. CASCADE pillars were successfully integrated to generate 18 initial and 6 final hypotheses, which were translated to 19 decisional changes.</p><p><strong>Conclusions: </strong>The CASCADE approach was an effective tool for rapidly, efficiently making patient-centered decisions during an ongoing, federally funded clinical trial. Opportunities for further development will include exploring best-practice structural procedures, enhancing greater opportunities for pre-panel input by community interest-holders, and determining how to best standardize CASCADE outputs.</p><p><strong>Trial registration: </strong>The CASCADE procedure was developed in the context of NCT05999448.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"168"},"PeriodicalIF":3.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12211934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538535","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}
引用次数: 0
Optimising dynamic treatment regimens using sequential multiple assignment randomised trials data with missing data. 利用缺失数据的顺序多任务随机试验数据优化动态治疗方案。
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-01 DOI: 10.1186/s12874-025-02595-1
Jessica Xu, Anurika P De Silva, Katherine J Lee, Robert K Mahar, Julie A Simpson
{"title":"Optimising dynamic treatment regimens using sequential multiple assignment randomised trials data with missing data.","authors":"Jessica Xu, Anurika P De Silva, Katherine J Lee, Robert K Mahar, Julie A Simpson","doi":"10.1186/s12874-025-02595-1","DOIUrl":"10.1186/s12874-025-02595-1","url":null,"abstract":"<p><p>Dynamic treatment regimens are commonly used for patients with chronic or progressive medical conditions. Sequential multiple assignment randomised trials (SMARTs) are studies used to optimise dynamic treatment regimens by repeatedly randomising participants to treatments. Q-learning, a stage-wise regression-based method used to analyse SMARTs, uses backward induction to compare treatments administered as a sequence. Missing data is a common problem in randomised trials and can be complex in SMARTs given the sequential randomisation. Common methods for handling missing data such as complete case analysis (CCA) and multiple imputation (MI) have been widely explored in single-stage randomised trials, however, the only study that explored these methods in SMARTs did not consider Q-learning. We evaluated the performance of CCA and MI on the estimation of Q-learning parameters in a SMART. We simulated 1000 datasets of 500 participants, based on a SMART with two stages, under different missing data scenarios defined by missing directed acyclic graphs (m-DAGS), percentages of missing data (20%, 40%), stage 2 treatment effects, and strengths of association with missingness in stage 2 treatment, patient history and outcome. We also compared CCA and MI using retrospective data from a longitudinal smoking cessation SMART. When there was no treatment effect at either stage 1 or 2, we observed close to zero absolute bias in the stage 1 treatment effect and similar empirical standard errors for CCA and MI under all missing data scenarios. When all participants had a relatively large stage 2 treatment effect, we observed minimal bias from both CCA and MI, with slightly greater bias for MI. Empirical standard errors were higher for MI compared to CCA under all scenarios except for when data were missing not dependent on any variables. When the stage 2 treatment effect varied between participants and data were missing dependent on other variables (for example, stage 1 responder status missing dependent on stage 1 treatment and baseline variables), we observed greater bias for MI when estimating the stage 1 treatment effect, which increased with the percentage missingness, while the bias for CCA remained minimal. Resulting empirical standard errors were lower or similar for MI compared to CCA under all missing data scenarios. Results showed that for a two-stage SMART, MI failed to capture the differences between treatment effects when the stage 2 treatment effect varied between participants.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"162"},"PeriodicalIF":3.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12211643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538475","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}
引用次数: 0
Over- and under-estimation of vaccine effectiveness. 对疫苗有效性的高估和低估。
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-01 DOI: 10.1186/s12874-025-02611-4
Hilla De-Leon, Dvir Aran
{"title":"Over- and under-estimation of vaccine effectiveness.","authors":"Hilla De-Leon, Dvir Aran","doi":"10.1186/s12874-025-02611-4","DOIUrl":"10.1186/s12874-025-02611-4","url":null,"abstract":"<p><strong>Background: </strong>The effectiveness of SARS-CoV-2 vaccines against infection has been a subject of debate, with varying results reported in different studies, ranging from 60-95% vaccine effectiveness (VE). This range is striking when comparing two studies conducted in Israel at the same time, as one study reported VE of 90-95%, while the other study reported only ~ 80%. We argue that this variability is due to inadequate accounting for indirect protection provided by vaccines, which can block further transmission of the virus.</p><p><strong>Materials and methods: </strong>We developed a novel analytic heterogenous infection model and extended our agent-based model of disease spread to allow for heterogenous interactions between vaccinated and unvaccinated across close-contacts and regions. We applied these models on real-world regional data from Israel from early 2021 to estimate VE using two common study designs: population-based and secondary infections.</p><p><strong>Results: </strong>Our results show that the estimated VE of a vaccine with efficacy of 85% can range from 70-95% depending on the interactions between vaccinated and unvaccinated individuals. Since different study designs capture different levels of interactions, we suggest that this interference explains the variability across studies. Finally, we propose a methodology for more accurate estimation without knowledge of interactions. DISCUSSIONS AND CONCLUSIONS: Our study highlights the importance of considering indirect protection when estimating vaccine effectiveness, explains how different study designs may report biased estimations, and propose a method to overcome this bias. We hope that our models will lead to more accurate understanding of the impact of vaccinations and inform public health policy.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"163"},"PeriodicalIF":3.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538476","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}
引用次数: 0
Transparency in epidemiological analyses of cohort data a case study of the Norwegian mother, father, and child cohort study (MoBa). 队列数据流行病学分析的透明度:挪威母亲、父亲和儿童队列研究(MoBa)的案例研究。
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-01 DOI: 10.1186/s12874-025-02601-6
Timo Roettger, Adrian Dahl Askelund, Viktoria Birkenæs, Ludvig Daae Bjørndal, Agata Bochynska, Bernt Damian Glaser, Tamara Kalandadze, Max Korbmacher, Ivana Malovic, Julien Mayor, Pravesh Parekh, Daniel S Quintana, Laurie J Hannigan
{"title":"Transparency in epidemiological analyses of cohort data a case study of the Norwegian mother, father, and child cohort study (MoBa).","authors":"Timo Roettger, Adrian Dahl Askelund, Viktoria Birkenæs, Ludvig Daae Bjørndal, Agata Bochynska, Bernt Damian Glaser, Tamara Kalandadze, Max Korbmacher, Ivana Malovic, Julien Mayor, Pravesh Parekh, Daniel S Quintana, Laurie J Hannigan","doi":"10.1186/s12874-025-02601-6","DOIUrl":"10.1186/s12874-025-02601-6","url":null,"abstract":"<p><strong>Background: </strong>Epidemiological research is central to our understanding of health and disease. Secondary analysis of cohort data is an important tool in epidemiological research but is vulnerable to practices that can reduce the validity and robustness of results. As such, adopting measures to increase the transparency and reproducibility of secondary data analysis is paramount to ensuring the robustness and usefulness of findings. The uptake of such practices has not yet been systematically assessed.</p><p><strong>Methods: </strong>Using the Norwegian Mother, Father, and Child Cohort study (MoBa; [23, 24]) as a case study, we assessed the prevalence of the following reproducible practices in publications between 2007-2023: preregistering secondary analyses, sharing of synthetic data, additional materials, and analysis scripts, conducting robustness checks, directly replicating previously published studies, declaring conflicts of interest and publishing publicly available versions of the paper.</p><p><strong>Results: </strong>Preregistering secondary data analysis was only found in 0.4% of articles. No articles used synthetic data sets. Sharing practices of additional data (2.3%), additional materials (3.4%) and analysis scripts (4.2%) were rare. Several practices, including data and analysis sharing, preregistration and robustness checks became more frequent over time. Based on these assessments, we present a practical example for how researchers might improve transparency and reproducibility of their research.</p><p><strong>Conclusions: </strong>The present assessment demonstrates that some reproducible practices are more common than others, with some practices being virtually absent. In line with a broader shift towards open science, we observed an increasing use of reproducible research practices in recent years. Nonetheless, the large amount of analytical flexibility offered by cohorts such as MoBa places additional responsibility on researchers to adopt such practices with urgency, to both ensure the robustness of their findings and earn the confidence of those using them. A particular focus in future efforts should be put on practices that help mitigating bias due to researcher degrees of freedom - namely, preregistration, transparent sharing of analysis scripts, and robustness checks. We demonstrate by example that challenges in implementing reproducible research practices in analysis of secondary cohort data-even including those associated with data sharing-can be meaningfully overcome.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"171"},"PeriodicalIF":3.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538478","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}
引用次数: 0
Evaluating blood sampling strategies within the SIREN study: the experience from a large cohort of healthcare workers in the UK. 在SIREN研究中评估血液采样策略:来自英国一大群卫生保健工作者的经验。
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-01 DOI: 10.1186/s12874-025-02599-x
Nipunadi Hettiarachchi, Debbie Blick, Tom Coleman, Ashley Otter, Angela Dunne, Jameel Khawan, Ezra Linley, Michelle J Cole, Michelle Cairns, Jasmin Islam, Sarah Foulkes, Susan Hopkins, Victoria Hall, Ana Atti
{"title":"Evaluating blood sampling strategies within the SIREN study: the experience from a large cohort of healthcare workers in the UK.","authors":"Nipunadi Hettiarachchi, Debbie Blick, Tom Coleman, Ashley Otter, Angela Dunne, Jameel Khawan, Ezra Linley, Michelle J Cole, Michelle Cairns, Jasmin Islam, Sarah Foulkes, Susan Hopkins, Victoria Hall, Ana Atti","doi":"10.1186/s12874-025-02599-x","DOIUrl":"10.1186/s12874-025-02599-x","url":null,"abstract":"<p><strong>Background: </strong>Delivering research studies that require a large number of samples to monitor specific populations is complex, often resulting in high costs and intricate logistics. We aim to describe the processes for blood sample collection and management and evaluate alternative sampling methods within a large cohort of healthcare workers in the UK (the SIREN study).</p><p><strong>Methods: </strong>We conducted a process evaluation. First, we described blood sample collection and management across different study periods from June 2020 to March 2024 and how these evolved over time. Secondly, we compared alternative methods of blood sampling: venous phlebotomy (hospital-based) vs. capillary sampling (at-home).</p><p><strong>Results: </strong>The main challenges with blood sampling within SIREN stemmed from the scale and use of decentralised phlebotomy across 135 hospital sites during the COVID-19 pandemic. We adapted our sampling processes as the study progressed, overcoming most of these challenges. When comparing hospital-based and at-home sampling, overall, return rates of samples taken at home were higher than site- based samples (80% vs 71%, respectively). At-home samples took less time to be returned to UKHSA Laboratory for testing compared to hospital-based samples (median 2 days; interquartile (IQ) 2-3) vs 6 days; IQ 3-8). However, at-home samples were more likely to be considered void (4%) when tested compared to hospital-based samples (0%). Cost for hospital-based sampling was almost 3-times higher than at-home sampling (£34.05 vs £11.50, respectively), although larger sample volumes were obtained via hospital-based sampling when compared to at-home sampling (8 ml vs 600 µl of whole blood).</p><p><strong>Conclusions: </strong>Sample collection and management in large scale research studies are complex. Our results support at-home blood sampling as an effective and cheaper strategy when compared to hospital-based phlebotomy and therefore should be considered as alternative sampling method for future research.</p><p><strong>Trial registration number: </strong>ISRCTN11041050-registration date 12/01/2021.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"165"},"PeriodicalIF":3.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12211489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538536","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}
引用次数: 0
Evaluation of changes in prediction modelling in biomedicine using systematic reviews. 用系统评价评价生物医学预测模型的变化。
IF 3.9 3区 医学
BMC Medical Research Methodology Pub Date : 2025-07-01 DOI: 10.1186/s12874-025-02605-2
Lara Lusa, Franziska Kappenberg, Gary S Collins, Matthias Schmid, Willi Sauerbrei, Jörg Rahnenführer
{"title":"Evaluation of changes in prediction modelling in biomedicine using systematic reviews.","authors":"Lara Lusa, Franziska Kappenberg, Gary S Collins, Matthias Schmid, Willi Sauerbrei, Jörg Rahnenführer","doi":"10.1186/s12874-025-02605-2","DOIUrl":"10.1186/s12874-025-02605-2","url":null,"abstract":"<p><p>The number of prediction models proposed in the biomedical literature has been growing year on year. In the last few years there has been an increasing attention to the changes occurring in the prediction modeling landscape. It is suggested that machine learning techniques are becoming more popular to develop prediction models to exploit complex data structures, higher-dimensional predictor spaces, very large number of participants, heterogeneous subgroups, with the ability to capture higher-order interactions. We examine the changes in modelling practices by investigating a selection of systematic reviews on prediction models published in the biomedical literature. We selected systematic reviews published between 2020 and 2022 which included at least 50 prediction models. Information was extracted guided by the CHARMS checklist. Time trends were explored using the models published since 2005. We identified 8 reviews, which included 1448 prediction models published in 887 papers. The average number of study participants and outcome events increased considerably between 2015 and 2019 but remained stable afterwards. The number of candidate and final predictors did not noticeably increase over the study period, with a few recent studies using very large numbers of predictors. Internal validation and reporting of discrimination measures became more common, but assessing calibration and carrying out external validation were less common. Information about missing values was not reported in about half of the papers, however the use of imputation methods increased. There was no sign of an increase in using of machine learning methods. Overall, most of the findings were heterogeneous across reviews. Our findings indicate that changes in the prediction modeling landscape in biomedicine are smaller than expected and that poor reporting is still common; adherence to well established best practice recommendations from the traditional biostatistics literature is still needed. For machine learning best practice recommendations are still missing, whereas such recommendations are available in the traditional biostatistics literature, but adherence is still inadequate.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"167"},"PeriodicalIF":3.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12211957/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538537","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}
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