BMC Medical Research Methodology最新文献

{"title":"Barriers and facilitators to conducting randomised controlled trials within routine care of neurorehabilitation centres: a qualitative study.","authors":"Isabella Hotz, Nathalie Ernst, Christian Brenneis, Gudrun Diermayr, Barbara Seebacher","doi":"10.1186/s12874-024-02386-0","DOIUrl":"10.1186/s12874-024-02386-0","url":null,"abstract":"<p><strong>Background: </strong>Randomised controlled trials (RCTs) are considered the gold standard for generating clinical evidence. The focus on high internal validity in RCTs challenges the external validity and generalisability of findings, potentially hindering their application in routine care. In neurorehabilitation, limited literature addresses conducting RCTs feasibly and efficiently. We investigated barriers and facilitators to conducting RCTs within routine care of neurorehabilitation centres from the perspective of stakeholders in neurorehabilitation in Germany and Austria.</p><p><strong>Methods: </strong>We conducted semi-structured interviews with stakeholders in neurorehabilitation from four centres in Germany and Austria, informed by the Theoretical Domains Framework (TDF) and the Capability, Opportunity, Motivation and Behaviour model (COM-B). Employing a hybrid approach, the interview analysis integrated both deductive, theory-driven analysis based on the TDF domains and COM-B model and inductive, reflexive thematic analysis.</p><p><strong>Results: </strong>Twelve stakeholders (4 physicians, 4 therapy managers, 4 therapists; 5 females, 7 males; with research experience spanning 0-40 years) were interviewed. Key barriers to conducting RCTs in neurological rehabilitation centres include limited financial, human, and time resources, high clinical workloads, and a lack of interest of some therapists. Ineffective leadership, perceived lack of research expertise, and communication issues were also significant barriers. Social influence factors such as lack of employer support and inadequate training access further contributed to the challenges. Additionally, barriers included insufficient research infrastructure, limited space, internal power struggles, and rigid cost bearer specifications. Key facilitators included physicians' and therapists' motivation to advance the field, contribute to knowledge, and to prioritise patient health. Support from supervisors, joint decision-making, and efficient organisation were crucial facilitators. Flexible therapy planning, mutual support, and interdisciplinary collaboration also played important roles.</p><p><strong>Conclusion: </strong>Our results suggest that increasing professional development and understanding, along with providing adequate financial, human, time, and spatial resources to support research endeavours, implementing effective communication strategies to enhance interdisciplinary collaboration and coordination among team members may contribute to increased motivation and facilitate RCTs within the setting of neurorehabilitation centres.</p><p><strong>Trial registration: </strong>This study was prospectively registered with the German Clinical Trials Register (08.04.2021 DRKSID DRKS00024982).</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"258"},"PeriodicalIF":3.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142543590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond hazard ratios: appropriate statistical methods for quantifying the clinical effectiveness of immune-oncology therapies - the example of the Netherlands.","authors":"Isaac Corro Ramos, Venetia Qendri, Maiwenn Al","doi":"10.1186/s12874-024-02373-5","DOIUrl":"10.1186/s12874-024-02373-5","url":null,"abstract":"<p><strong>Background: </strong>The Dutch Committee for the Evaluation of Oncological Drugs evaluates the effectiveness of new oncological treatments. The committee compares survival endpoints to the so-called PASKWIL-2023 criteria for palliative treatments, which define if treatment effects are considered clinically relevant. A positive recommendation depends on whether the median overall survival (OS) is below or above 12 months in the comparator arm. If the former applies, an OS benefit of at least 12 weeks, and a hazard ratio (HR) smaller than 0.7 are required. If the latter applies, an OS or progression free survival (PFS) benefit of at least 16 weeks, and an HR smaller than 0.7 are required. Nonetheless, the median survival time may not be reached and the proportional hazards (PH) assumption, quantified by the HR, is likely violated for immuno-oncology (IO) therapies, deeming these criteria inappropriate.</p><p><strong>Methods: </strong>We conducted a systematic literature review to identify statistical methods used to represent the clinical effectiveness of IO therapies based on trial data. We searched MEDLINE and EMBASE databases from inception to August 31, 2022, limited to English papers. Methodological studies, randomized controlled trials, and discussion papers recognising key issues of survival data analysis of IO therapies were eligible for inclusion.</p><p><strong>Results: </strong>A total of 1,035 unique references were identified. After full paper screening, 17 publications were included in the review. Additionally, 43 papers were identified through 'snowballing'. We conclude that the current PASKWIL-2023 criteria are methodologically incorrect under non-PH. In that case, single summary statistics fail to capture the treatment effect and any measure should be interpreted in combination with the Kaplan-Meier curves. We recommend 'parameter-free' measures, such as the difference in restricted mean survival time, avoiding assumptions on the underlying survival.</p><p><strong>Conclusions: </strong>The HR is commonly used to assess treatment effectiveness, without investigating the validity of the PH assumption. This happens with the application of the PASKWIL-2023 criteria for palliative oncology treatments, which can only be valid under a PH setting. Under non-PH, alternative treatment effect measures are suggested. We propose a step-by-step approach supporting the choice of the most appropriate methods to quantify treatment effectiveness that can be used to redefine the PASKWIL-2023 criteria, or similar criteria in other clinical areas.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"260"},"PeriodicalIF":3.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142543600","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":"Characteristics of attrition within the SuperMIX cohort of people who inject drugs: a multiple event discrete-time survival analysis.","authors":"Shady Abdelsalam, Paul A Agius, Rachel Sacks-Davis, Amanda Roxburgh, Michael Livingston, Lisa Maher, Matthew Hickman, Paul Dietze","doi":"10.1186/s12874-024-02377-1","DOIUrl":"10.1186/s12874-024-02377-1","url":null,"abstract":"<p><strong>Background: </strong>Compared to the general population, people who inject drugs have poor health and wellbeing. Longitudinal studies can provide insight into factors driving these worse health outcomes but are subject to methodological challenges, such as cohort attrition. The aim of this study was to assess and characterise attrition in a prospective cohort of people who inject drugs in Victoria, Australia.</p><p><strong>Methods: </strong>Using annually collected self-reported data from The Melbourne Injecting Drug User Cohort Study (SuperMIX) from September 2008 to January 2021, we estimated the incidence of participants being lost-to-follow-up (LTFU), with an episode of being LTFU defined as participants not undertaking a follow-up interview within two years of their last interview. We utilised a multiple event discrete-time survival analysis on participant period-observation data to estimate the associations between key factors and LTFU. Key areas of exposure measurement in analyses were sociodemographic, drug use and mental health.</p><p><strong>Results: </strong>A total of n = 1328 SuperMIX participants completed a baseline interview, with n = 489 (36.8%) LTFU, i.e. not completing a follow-up interview in the following two years. Increased attrition was observed among SuperMIX participants who were: born outside Australia, younger than 30 years, reporting having completed fewer years of education, not residing in stable accommodation, not in stable employment and not on opioid agonist therapy (OAT).</p><p><strong>Conclusions: </strong>The attrition rate of the SuperMIX cohort has largely been stable throughout the duration of the study. Higher attrition rates among individuals at greater sociodemographic disadvantage and not on OAT suggest that additional efforts are required to retain these participants. Findings also suggest that SuperMIX might not be capturing data on adverse health and wellbeing outcomes among subpopulations at high risk of harm.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"257"},"PeriodicalIF":3.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142543601","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":"Towards realist-informed ripple effects mapping (RREM): positioning the approach.","authors":"Kevin Harris, James Nobles, Louis Ryan, Christoph Szedlak, Hannah Taylor, Rowena Hawkins, Alice Cline, Elizabeth Smith, Amelia Hall","doi":"10.1186/s12874-024-02371-7","DOIUrl":"10.1186/s12874-024-02371-7","url":null,"abstract":"<p><strong>Background: </strong>Evaluation approaches such as ripple effects mapping (REM) and realist evaluation have emerged as popular methodologies to evidence impact, and the processes of change within public health as part of whole systems approaches. Despite the various examples of their implementation across different evaluation settings, there has been little or no evidence of how they might be effective when combined.</p><p><strong>Methods: </strong>With REM's potential to pragmatically illustrate impact, and realist evaluation's strength to identify how and why impacts emerge, this paper develops a rationale and process for their amalgamation. Following this, we outline a realist-informed ripple effects mapping (RREM) protocol drawing upon a physical activity based case study in Essex that may be suitable for application within evaluation settings in a range of public health, whole system and physical activity settings.</p><p><strong>Discussion: </strong>Combining these two approaches has the potential to more effectively illuminate the impacts that we see within public health and whole system approaches and initiatives. What is more, given the complexity often imbued within these approaches and initiatives, they hold capability for also capturing the causal mechanisms that explain these impacts.</p><p><strong>Conclusions: </strong>It is our conclusion that when combined, this novel approach may help to inspire future research as well as more effective evaluation of public health and whole system approaches. This is crucial if we are to foster a culture for learning, refinement and reflection.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"259"},"PeriodicalIF":3.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142543603","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":"Interpretation of statistical findings in randomised trials: a survey of statisticians using thematic analysis of open-ended questions.","authors":"Karla Hemming, Laura Kudrna, Sam Watson, Monica Taljaard, Sheila Greenfield, Beatriz Goulao, Richard Lilford","doi":"10.1186/s12874-024-02366-4","DOIUrl":"10.1186/s12874-024-02366-4","url":null,"abstract":"<p><strong>Background: </strong>Dichotomisation of statistical significance, rather than interpretation of effect sizes supported by confidence intervals, is a long-standing problem.</p><p><strong>Methods: </strong>We distributed an online survey to clinical trial statisticians across the UK, Australia and Canada asking about their experiences, perspectives and practices with respect to interpretation of statistical findings from randomised trials. We report a descriptive analysis of the closed-ended questions and a thematic analysis of the open-ended questions.</p><p><strong>Results: </strong>We obtained 101 responses across a broad range of career stages (24% professors; 51% senior lecturers; 22% junior statisticians) and areas of work (28% early phase trials; 44% drug trials; 38% health service trials). The majority (93%) believed that statistical findings should be interpreted by considering (minimal) clinical importance of treatment effects, but many (61%) said quantifying clinically important effect sizes was difficult, and fewer (54%) followed this approach in practice. Thematic analysis identified several barriers to forming a consensus on the statistical interpretation of the study findings, including: the dynamics within teams, lack of knowledge or difficulties in communicating that knowledge, as well as external pressures. External pressures included the pressure to publish definitive findings and statistical review which can sometimes be unhelpful but can at times be a saving grace. However, the concept of the minimally important difference was identified as a particularly poorly defined, even nebulous, construct which lies at the heart of much disagreement and confusion in the field.</p><p><strong>Conclusion: </strong>The majority of participating statisticians believed that it is important to interpret statistical findings based on the clinically important effect size, but report this is difficult to operationalise. Reaching a consensus on the interpretation of a study is a social process involving disparate members of the research team along with editors and reviewers, as well as patients who likely have a role in the elicitation of minimally important differences.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"256"},"PeriodicalIF":3.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142543602","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":"Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes.","authors":"Kenneth Menglin Lee, Grace Meijuan Yang, Yin Bun Cheung","doi":"10.1186/s12874-024-02379-z","DOIUrl":"10.1186/s12874-024-02379-z","url":null,"abstract":"<p><strong>Background: </strong>The fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns. It was previously unclear whether unexposed clusters would contribute to the estimation of the intervention effect in a fixed effects analysis. However, recent work has demonstrated that including an unexposed cluster can improve the precision of the intervention effect estimator in a fixed effects analysis of SW-CRTs with continuous outcomes. Still, SW-CRTs are commonly designed with binary outcomes and it is unknown if those previous results extend to SW-CRTs with non-continuous outcomes.</p><p><strong>Methods: </strong>In this article, we mathematically prove that the inclusion of unexposed clusters improves the precision of the fixed effects intervention effect estimator for SW-CRTs with binary and count outcomes. We then explore the benefits of including an unexposed cluster in simulated datasets with binary or count outcomes and a real palliative care data example with binary outcomes.</p><p><strong>Results: </strong>The simulations show that including unexposed clusters leads to tangible improvements in the precision, power, and root mean square error of the intervention effect estimator. The inclusion of the unexposed cluster in the SW-CRT of a novel palliative care intervention with binary outcomes yielded smaller standard errors and narrower 95% Wald Confidence Intervals.</p><p><strong>Conclusions: </strong>In this article, we demonstrate that the inclusion of unexposed clusters in the fixed effects analysis can lead to the improvements in precision, power, and RMSE of the fixed effects intervention effect estimator for SW-CRTs with binary or count outcomes.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"254"},"PeriodicalIF":3.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521022","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":"Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature.","authors":"Laetitia de Abreu Nunes, Richard Hooper, Patricia McGettigan, Rachel Phillips","doi":"10.1186/s12874-024-02369-1","DOIUrl":"10.1186/s12874-024-02369-1","url":null,"abstract":"<p><strong>Background: </strong>In randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships.</p><p><strong>Methods: </strong>We conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings.</p><p><strong>Results: </strong>We identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used.</p><p><strong>Conclusions: </strong>We found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"253"},"PeriodicalIF":3.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521023","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":"Impact of selective reporting bias on stroke trials: potential compromise in evidence synthesis - A cross-sectional study.","authors":"Xinyao Wang, Youlin Long, Na Zhang, Xinyi Wang, Qiong Guo, Ya Deng, Jin Huang, Liang Du","doi":"10.1186/s12874-024-02381-5","DOIUrl":"10.1186/s12874-024-02381-5","url":null,"abstract":"<p><strong>Background: </strong>Accurate reporting of outcomes is crucial for interpreting the results of randomized controlled trials (RCTs). However, selectively reporting outcomes in publications to achieve researchers' anticipated results still occurs frequently. This study aims to investigate the prevalence of selective reporting of outcomes in RCTs on treating acute ischemic stroke (AIS), identify factors contributing to this issue, and assess its potential impact on the degree and direction of intervention effect.</p><p><strong>Methods: </strong>A search was conducted in MEDLINE, Embase, and the Cochrane Library to collect interventional RCTs on AIS published from 2020 to 2022. Full texts of RCTs were reviewed, and only those reporting International Clinical Trials Registry Platform primary registry numbers were included. Registration information of the RCTs was extracted from the registry platforms and compared with the publications' details to assess the selective reporting of outcomes. Bayesian multilevel logistic regression was used to analyze the reasons behind selective reporting.</p><p><strong>Results: </strong>Among the total of 159 AIS RCTs identified, 82 (51.6%) were ultimately included, as they reported registration numbers, which encompassed 819 outcomes. Among them, 72 RCTs (87.8%) and 497 outcomes (60.7%) exhibited selective reporting. Omission-type selective reporting (downgrading, omitting, or ambiguously reporting) accounted for 36.4%, while addition-type selective reporting (upgrading, adding, or altering the measurement scope of outcomes) comprised 63.6%. Omission-type selective reporting correlated with negative results (OR: 7.39; 95% CI: 4.08-13.44), whereas addition-type selective reporting correlated with positive results (OR: 2.07; 95% CI: 1.34-3.26) and publication in journals that are not in the top quartile of the Journal Citation Reports (OR: 2.48; 95% CI: 1.15-5.38).</p><p><strong>Conclusions: </strong>Registered interventional AIS RCTs still face significant issues regarding selective reporting of outcomes. Therefore, it is necessary to further evaluate the influence of selective reporting bias on the positive results obtained from individual AIS RCTs and the systematic reviews based on these RCTs.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"255"},"PeriodicalIF":3.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514957/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521021","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":"Estimating reference intervals from an IPD meta-analysis using quantile regression.","authors":"Ziren Jiang, Haitao Chu, Zhen Wang, M Hassan Murad, Lianne K Siegel","doi":"10.1186/s12874-024-02378-0","DOIUrl":"10.1186/s12874-024-02378-0","url":null,"abstract":"<p><strong>Background: </strong>Reference intervals, which define an interval in which a specific proportion of measurements from a healthy population are expected to fall, are commonly used in medical practice. Synthesizing information from multiple studies through meta-analysis can provide a more precise and representative reference interval than one derived from a single study. However, the current approaches for estimating the reference interval from a meta-analysis mainly rely on aggregate data and require parametric distributional assumptions that cannot always be checked.</p><p><strong>Methods: </strong>With the availability of individual participant data (IPD), non-parametric methods can be used to estimate reference intervals without any distributional assumptions. Furthermore, patient-level covariates can be introduced to estimate personalized reference intervals that may be more applicable to specific patients. This paper introduces quantile regression as a method to estimate the reference interval from an IPD meta-analysis under the fixed effects model.</p><p><strong>Results: </strong>We compared several non-parametric bootstrap methods through simulation studies to account for within-study correlation. Under fixed effects model, we recommend keeping the studies fixed and only randomly sampling subjects with replacement within each study.</p><p><strong>Conclusion: </strong>We proposed to use the quantile regression in the IPD meta-analysis to estimate the reference interval. Based on the simulation results, we identify an optimal bootstrap strategy for estimating the uncertainty of the estimated reference interval. An example of liver stiffness measurements, a clinically important diagnostic test without explicitly established reference range in children, is provided to demonstrate the use of quantile regression in estimating both overall and subject-specific reference intervals.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"251"},"PeriodicalIF":3.9,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495023","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":"Office-based risk equation of Globorisk for prediction of ten-years cardiovascular risk among Iranian population: findings from Fasa PERSIAN cohort study.","authors":"Amir Baseri, Azizallah Dehghan, Rozhan Khezri, Zahra Montaseri, Dagfinn Aune, Fatemeh Rezaei","doi":"10.1186/s12874-024-02374-4","DOIUrl":"10.1186/s12874-024-02374-4","url":null,"abstract":"<p><strong>Background: </strong>Globorisk is one of the prediction tools for 10-year risk assessment of cardiovascular disease, featuring an office-based (non-laboratory-based) version. This version does not require laboratory tests for determining the CVD risk. The present study aims to determine the 10-year CVD risk using the office-based Globorisk model and factors associated with the 10-year CVD risk.</p><p><strong>Methods: </strong>In this study, baseline data from 6810 individuals participating in the Fasa cohort study, with no history of CVD or stroke, were utilized. The risk equation of the office-based Globorisk model incorporates age, sex, systolic blood pressure (SBP), body mass index (BMI), and smoking status. The Globorisk model categorizes the risk into three groups: low risk (< 10%), moderate risk (10% to < 20%), and high risk (≥ 20%). To identify factors associated with the 10-year CVD risk, the predicted risk was categorized into two groups: <10% and ≥ 10%. Multivariable logistic regression analysis was employed to determine factors associated with an increased CVD risk.</p><p><strong>Results: </strong>According to the 10-year CVD risk categorization, 78.3%, 16.4%, and 5.3% of men were in the low, moderate, and high risk groups, respectively, while 85.8%, 10.0%, and 4.2%, of women were in the respective risk groups. Multivariable logistic regression results indicated that in men, the 10-year CVD risk decreases with being an opium user, and increases with being illiterate, having abdominal obesity, and low or moderate physical activity compared to high physical activity. In women, being married, and higher fiber consumption decrease the 10-year CVD risk, while being illiterate, low or moderate physical activity compared to high physical activity, having abdominal obesity, opium use, and being in wealth quintiles 1 to 4 compared to quintile 5 increase the risk.</p><p><strong>Conclusions: </strong>Considering the factors associated with increased CVD risk, there is a need to enhance awareness and modify lifestyle to mitigate and reduce the risk of CVD. Additionally, early identification of individuals at moderate to high risk is essential for preventing disease progression. The use of the office-based Globorisk model can be beneficial in settings where resources are limited for determining the 10-year CVD risk.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"252"},"PeriodicalIF":3.9,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495026","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}