BMC Medical Research MethodologyBMC医学研究方法学
BMC Medical Research Methodology(英文缩写 BMC MED RES METHODOL),ISSN 1471-2288,eISSN 1471-2288,中文译名:BMC医学研究方法学 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
BMC Medical Research Methodology 最新收录文献
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1. Quantifying uncertainty: a practical comparison of Bayesian and frequentist meta-analytic frameworks applied to lead exposure and children's IQ.
PMID:日期:2026-09-24Uncertainty quantification is central to evidence synthesis for risk assessment and policy-making. Frequentist meta-analysis - currently predominant in epidemiology - quantifies uncertainty through confidence intervals, while Bayesian meta-analysis provides posterior distributions. We performed frequentist and Bayesian meta-analyses to compare uncertainty quantification and applicability for risk assessment using lead exposure and children's IQ as a case study. We systematically searched PubMed and Scopus for epidemiological studies on blood lead level (BLL) and IQ score in children. After screening, risk of bias (RoB) in the identified studies was assessed and regression coefficients were harmonised to the [Formula: see text] scale. In both meta-analyses, we included the same effect estimates, assumed a multilevel structure and used random-effects models. The Bayesian meta-analysis employed a hierarchical model with weakly-informative priors. Sensitivity analyses examined robustness to RoB and prior specification. [Formula: see text] and [Formula: see text] were estimated as measures of between-study heterogeneity. Twelve studies were included in the meta-analysis. The frequentist model yielded a pooled estimate of -2.45 (95% confidence interval: -3.82 to -1.08) IQ points per unit increase in [Formula: see text]-transformed BLL (µg/dL) and a 95% predictive interval of -6.53 to 1.53. The Bayesian model yielded a mean pooled estimate of -2.28 (95% credible interval: -3.54 to -1.04) and a 95% predictive interval of -6.28 to 1.69. Between-study heterogeneity was substantial (frequentist: [Formula: see text] = 3.32, [Formula: see text] = 85.0%; Bayesian: posterior median [Formula: see text] = 3.26, [Formula: see text] = 84.7%). The Bayesian approach provided a full posterior distribution for heterogeneity, directly quantifying uncertainty from between-study variation. Sensitivity analyses showed both meta-analyses were robust to RoB, and Bayesian estimates were insensitive to tested priors. Depending on the specified prior configuration, the Bayesian model was less sensitive to studies reporting substantial uncertainty, resulting in a lower pooled effect compared to the frequentist model. This regularisation is particularly valuable in small meta-analyses, where frequentist heterogeneity estimates can be unstable. We provide updated pooled estimates for the effect of lead on children's IQ. The Bayesian framework provides posterior distributions, enabling direct probability statements, e.g. about exceeding risk thresholds, and comparative assessments between pollutants. These features are particularly valuable for risk assessment and policy-making.
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2. Methods for analyzing recurrent and terminal events considering composite and competing endpoints in clinical trials: a practical overview.
PMID:日期:2026-09-12Time to event endpoints like time to myocardial infarction, relapse, or death are often of primary interest in clinical trials. To describe the whole disease process and gain a complete picture of treatment effects, methods for recurrent time to event analysis considering terminal events were proposed, i.e. methods considering all events that occur per individual. However, these methods are not often applied in clinical studies. This might be due to the researchers' uncertainties about underlying model assumptions and interpretation of results. We therefore aim to give a comprehensive overview of methods for the analysis of multiple time to events comparing model assumptions and interpretation. We illustrate the methods using a cardiovascular trial and provide the corresponding R code.
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3. Imputing longitudinal PROM data at the item and score level in clinical trials.
PMID:日期:2026-09-04Missing data in patient-reported outcome measures (PROMs) can affect estimation accuracy and inferential validity, particularly under missing-at-random (MAR) mechanisms. Multiple imputation (MI) is commonly recommended, but the relative performance of item-level and score-level imputation for longitudinal PROM composite scores remains insufficiently understood. We conducted a simulation study based on longitudinal clinical trial PROM data. Predictive mean matching (PMM) and random forest (RF) imputation were evaluated at both the item and score level under empirically calibrated MAR mechanisms. Simulation scenarios varied sample size, overall missingness, and the proportion of unit nonresponse. Performance was assessed using root mean squared error (RMSE), bias magnitude, relative efficiency compared with complete case analysis (CCA), conditional confidence interval coverage, and variance ratio estimates. RMSE decreased with increasing sample size and increased with higher proportions of missingness across all methods. Both PMM and RF generally outperformed CCA in terms of RMSE and relative efficiency, particularly under higher missingness. Differences between item-level and score-level imputation were generally modest, although score-level imputation tended to yield slightly lower RMSE and bias magnitudes under more severe missingness conditions. Conditional coverage remained close to nominal levels across most MI settings. Variance tended to be overestimated in some scenarios, although sensitivity analyses with larger numbers of imputations substantially reduced this effect. PMM showed comparatively stable performance across simulation settings. Multiple imputation methods generally outperformed complete case analysis for handling longitudinal PROM data under MAR. While score-level imputation sometimes showed slightly more favorable performance than item-level imputation when targeting composite score means, the magnitude of these differences was frequently small in practical terms. PMM provided stable performance across a wide range of settings and represents a reasonable default approach for many applied PROM analyses.
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4. Is there a limit to limitations? A proposal for structured limitations reporting in scientific manuscripts.
PMID:日期:2026-08-28Reporting studies limitations is essential for transparent interpretation. While reporting guidelines such as CONSORT, STROBE, PRISMA mandate disclosure of limitations, no widely adopted framework standardizes how these are reported in a structured and comparable format. In practice, limitations sections often extend over several paragraphs reiterating predictable design-related constraints. This limits comparability, contributes to inefficiency and may overshadow genuine study-specific considerations. We propose a semi-structured checklist combining standardized limitation domains with a concise narrative component. This approach aims to ensure minimum systematic disclosure while preserving critical interpretation. Structured reporting may improve clarity, comparability, and enable meta-research on limitations across studies. Consensus-based refinement and pilot implementation are warranted to evaluate feasibility and impact.
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5. A simulation-based comparison of Boruta, LASSO, and Elastic Net for variable selection in logistic regression, with an ovarian cancer miRNA application.
PMID:日期:2026-08-24Variable selection is a central challenge in logistic regression, particularly in high-dimensional biomedical applications where correlated predictors and limited sample sizes complicate reliable identification of relevant variables. This study aims to systematically compare three widely used variable selection approaches - Boruta, LASSO, and Elastic Net - under a range of data-generating conditions and to illustrate their performance using an ovarian cancer miRNA dataset. We conducted a simulation study across 36 logistic regression scenarios varying in sample size, dimensionality, predictor correlation, and effect magnitude. Performance was evaluated using true-positive and false-positive selection rates. In addition, all three methods were applied to a real-world serum miRNA expression dataset, and discriminative performance was assessed using the area under the receiver operating characteristic curve (AUC). Boruta, Elastic Net, and LASSO exhibited distinct variable selection behaviors across simulation scenarios. Boruta maintained strong true-positive recovery while controlling false positives in most settings, particularly when predictors were highly correlated. Elastic Net consistently achieved high sensitivity but produced comparatively large false-positive rates. LASSO showed the most conservative behavior, recovering fewer true predictors while maintaining low false-positive rates across nearly all scenarios. In the ovarian cancer miRNA application, all three methods achieved similarly strong test-set AUC performance, despite marked differences in the size of the selected biomarker panels. The results demonstrate clear trade-offs among the three methods. Boruta offers a favorable balance between sensitivity and specificity in highly correlated settings. Elastic Net prioritizes sensitivity at the cost of increased false discoveries, whereas LASSO provides stricter false-positive control with reduced sensitivity. These findings offer practical guidance for selecting variable selection methods in logistic regression, particularly for high-dimensional biomedical applications.
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7. Optimizing patient-reported sociodemographic measures in health care - a national example of the development and evaluation of a practical standardized instrument for collecting patient data in studies.
PMID:日期:2026-08-20The development of patient-oriented health care is continuous subject to old and new challenges. To better identify patient needs, studies are necessary and standardized data collection is essential to describe, analyze and compare data. Associated demographic variables remain a topic of discussion in health services research. Common approaches either differ in relevant sociodemographic aspects, or their standardization is too detailed to be feasibly used in studies with a clinical or health services research focus. Our aim was to develop and evaluate a comprehensive and practical instrument for collecting sociodemographic patient data in studies. A variant (interdisciplinary expert panel) of the nominal group technique was used as a consensus method, in addition, a focus group was held and a 2-phase pretest according to Prüfer and Rexroth (method: "Think Aloud") was used for evaluation. In the first phase we conducted 6 interviews with potential patients. The main phase was planned to enroll at least 200 patients to test the instrument with a quantitative sample as recommended for testing patient-reported outcome measures. It took place in eight departments of the University Medical Center Hamburg-Eppendorf. Exploratory analyses were conducted using SPSS. The interdisciplinary expert panel revised the instrument. In the main phase, 290 (51%) of 574 approached patients participated [median (minimum; maximum) age: 40 (19; 86) years; 55% female]. Of these respondents 66%/ 31%/ 3% considered the length of the questionnaire to be appropriate/ too long/ too short; 96% found it understandable. Seven questions on living and income situation (commented at least three times) showed a need for clarification and modification. Non-responders optionally provided reasons for decline of which 7 clusters could be formed (predominantly: time). Final standardized variables: biological sex; year/country of birth; marital status/biological children; living/household type/persons/age; highest school/ qualification/current profession; household income/specialties/persons; country of birth mother/father; nationalities/residence status/native languages/German skills. Patient involvement is helpful in the development of patient survey instruments. Our instrument is based on the patients' perspective and understanding of these measurements. The aim is to further strengthen patient-centered health care. The revised instrument will be used in studies. The expert group will continue to identify and implement socio-demographic developments.
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8. Validating methods for inferring co-occurring diseases: a flexible framework for simulating synthetic data.
PMID:日期:2026-08-19The validation of methods is an integral part of statistical research, defining conditions under which methods yield reliable results. Empirical validation requires a solid data basis to control and manage relevant characteristics like sample size, dimensionality, and underlying dependency structures. Real-world data often fails to meet these requirements, particularly in medical contexts where privacy regulations restrict availability. For this reason, synthetic data is an effective alternative for method validation. However, generating synthetic data is demanding when it must precisely mirror complex dependence structures while simultaneously controlling specific target characteristics. We address the medical context of co-occurring diseases, where symptoms may overlap or conflict. We propose a four-step framework to generate synthetic data for the simulation-based validation of statistical methods. The framework involves: (I) generating patient covariates; (II) connecting this information to predictors for single or joint disease occurrence; (III) transforming predictors into disease probabilities or scores; and (IV) converting these into disease occurrences. Each step offers several alternatives for modeling the overall dependence structure. We apply our framework to a case study of pain-causing diseases which share certain similarities in their clinical presentations, and which can occur either individually or jointly. By employing five combinations of methodological alternatives, we evaluate the approaches' ability to achieve target characteristics and demonstrate their specific strengths and weaknesses. Matching the data-generating process with the estimation method allows for the successful recovery of input information, such as coefficients and correlations. Target properties like disease prevalence and associations are achieved to varying degrees depending on the methods used. While the proposed theory-driven framework is broadly applicable beyond the specific medical use case, it relies on careful, domain-informed parameter curation to generate meaningful synthetic datasets. Its flexible, adjustable input settings enable researchers to tailor data generation to their precise methodological requirements, providing a controlled basis for simulation-based validation without implying direct clinical inference.
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9. Applications of causal and structural equation modeling in epidemiology: a systematic and critical review.
PMID:日期:2026-07-21Structural equation modeling (SEM) and causal modeling (CM) are powerful statistical approaches for identifying complex interrelationships among variables. However, their application in epidemiology remains limited and under-documented, especially in infectious disease research, which requires integrated analytical frameworks for effective control. To examine how SEM and CM have been applied, their methodological characteristics, and reporting practices, a systematic and critical review was conducted following PRISMA guidelines. The search covered studies published between 1987 and 2025 across PubMed, Scopus, Web of Science, ScienceDirect, SpringerLink, Google Scholar, and the Directory of Open Access Journals. After rigorous screening, 458 articles were thoroughly evaluated. Most studies focused on neuropsychiatric (32.1%) and chronic (30.1%) conditions, with few addressing infectious diseases (24.0%), primarily malaria, tuberculosis, and HIV, particularly in low-income countries where context-specific evidence is urgently needed to inform targeted interventions. SEM studies predominantly used maximum likelihood estimation (57.7%) and large samples ([Formula: see text] observations in 85%) with CB-SEM remaining the dominant approach across all sample size categories. In contrast, CM studies showed substantial variability in sample sizes across approaches, ranging from fewer than 100 to over 200 observations (coefficient of variation [Formula: see text]), with no consistent sample size threshold across methods. Methodological reporting was often incomplete, notably regarding study design (17.4%), measurement validity (15.8%), and model fit criteria (5.2%), reducing transparency and reproducibility. Overall, broader application of SEM and CM to infectious diseases, combined with improved methodological transparency, could substantially strengthen causal inference and guide evidence-based disease control strategies. Moreover, integrating longitudinal study designs would further enhance the robustness and interpretability of causal findings.
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10. A systematic review and meta synthesis of barriers and facilitators to Black women's participation in healthcare research.
PMID:日期:2026-07-21Black women are under-represented in clinical research, contributing to persistent health inequities and undermining the validity and generalisability of research findings. Understanding facilitators and barriers to their research engagement is essential to addressing these challenges. This study aimed to identify and synthesise evidence on barriers and facilitators which influence Black women's participation in healthcare research, to inform inclusive recruitment strategies and equitable research designs. This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO (CRD42024587308). Findings were narratively synthesised using thematic analysis and discussed iteratively through Patient and Public Involvement. Five databases were searched (CINAHL, PsycInfo, Embase, MIDIRS, MEDLINE) for studies published since 2010. Papers in any language were screened for inclusion. Patient and Public Involvement members informed review objectives and data interpretation. Primary research studies reporting on Black women's experiences, attitudes, and facilitators/barriers to participating in any type of healthcare research. Studies were included if ≥ 50% (to ensure primary representation) of the sample recruited were Black women. Seventy-two studies were included. Most were from America and spanned research areas relating to oncology, Human Immunodeficiency Virus (HIV), dementia, maternity, and general health. Five overarching themes identified how Black women's participation in healthcare research is shaped by a complexity of factors. These were mistrust, interpersonal experiences, altruism, self-interest, and low health/research literacy and awareness. Patient and Public Involvement consultations concurred that the findings aligned with a "bridge and barriers" analogy, illustrating how various factors can facilitate or prevent Black women from engaging with research opportunities. This provided a clear framework to guide culturally sensitive implementation for researchers, healthcare professionals, and policymakers. Inclusive strategies for recruiting Black women into research must focus on co-constructing a bridge between communities and research systems. This requires structural and systemic change in research delivery environments and ongoing collaborations with all stakeholders including Black women, healthcare system providers, and research teams. PROSPERO (CRD42024587308).