BMC Medical Research Methodology最新文献

{"title":"Assessing the replicability of RCTs in RWE emulations.","authors":"Jeanette Köppe, Charlotte Micheloud, Stella Erdmann, Rachel Heyard, Leonhard Held","doi":"10.1186/s12874-025-02589-z","DOIUrl":"10.1186/s12874-025-02589-z","url":null,"abstract":"<p><strong>Background: </strong>The standard regulatory approach to assess replication success is the two-trials rule, requiring both the original and the replication study to be significant with effect estimates in the same direction. The sceptical p-value was recently presented as an alternative method for the statistical assessment of the replicability of study results.</p><p><strong>Methods: </strong>We review the statistical properties of the sceptical p-value and compare those to the two-trials rule. We extend the methodology to non-inferiority trials and describe how to invert the sceptical p-value to obtain confidence intervals. We illustrate the performance of the different methods using real-world evidence emulations of randomized controlled trials (RCTs) conducted within the RCT DUPLICATE initiative.</p><p><strong>Results: </strong>The sceptical p-value depends not only on the two p-values, but also on sample size and effect size of the two studies. It can be calibrated to have the same Type-I error rate as the two-trials rule, but has larger power to detect an existing effect. In the application to the results from the RCT DUPLICATE initiative, the sceptical p-value leads to qualitatively similar results than the two-trials rule, but tends to show more evidence for treatment effects compared to the two-trials rule.</p><p><strong>Conclusion: </strong>The sceptical p-value represents a valid statistical measure to assess the replicability of study results and is useful in the context of real-world evidence emulations.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"141"},"PeriodicalIF":3.9,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12103786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141335","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":"Veteran participation in the All of Us Research Program: applying an intersectionality lens to evaluate participant diversity.","authors":"Lauren O Thomann, Katherine A Hanson, Jane Plomp, Jason L Vassy, Sherilyn J Sawyer","doi":"10.1186/s12874-025-02588-0","DOIUrl":"10.1186/s12874-025-02588-0","url":null,"abstract":"<p><strong>Background: </strong>Many segments of the population are underrepresented in biomedical research (UBR). Lack of diversity in health research limits understanding of individual and population level differences and risks the generalizability of study results. Examining intersectionality among VA-enrolled Veteran participants in the All of Us Research Program offers a first look at in-depth understanding of Veteran identity.</p><p><strong>Methods: </strong>Multi-modal approaches to engagement and recruitment were utilized to enroll a diverse cohort of Veterans nationally from 2018 to 2024. All of Us data were analyzed across eight UBR categories and their intersections to highlight the complexity of Veteran identity.</p><p><strong>Results: </strong>Veteran All of Us participants reflect the diversity of Veterans nationwide. All of Us participant metrics shed new light on the diversity of the Veteran population, with over 75 unique UBR combinations identified among participants, and over 90% of participants meeting the criteria for at least one UBR category.</p><p><strong>Conclusions: </strong>The use of a broad spectrum of engagement approaches was shown to be successful for reaching a more diverse Veteran base, and complex intersectional identities among Veterans are described. Greater understanding of intersectionality and its significance to representation can bolster the adaptation of Veteran engagement methodology in research and broader healthcare settings.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"136"},"PeriodicalIF":3.9,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141344","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":"Six solutions for clinical study data sharing in Germany.","authors":"Evgeny Bobrov, Christina Habermehl, Daniel Strech, Tracey Weissgerber, René Bernard","doi":"10.1186/s12874-025-02560-y","DOIUrl":"10.1186/s12874-025-02560-y","url":null,"abstract":"<p><p>Sharing clinical study data is endorsed by many funders and journals, international policy frameworks, and patients. Reuse of clinical study data demonstrably improves health research, and emerging technologies may enhance the value derived from shared data. Unfortunately, clinical research has failed to harness the transformative power of data sharing, and sharing remains the exception. This opinion piece focuses on the massive obstacles to sharing clinical study data in Germany, which results in very low sharing rates, wasted resources, and frustration among local researchers and international partners. We argue that this sharing crisis demands immediate and concerted action. As a remedy, we propose six feasible steps to boost clinical study data availability in Germany, derived from our experience consulting researchers and exploring solutions with international partners. Our recommendations target ethics committees, trial registries, infrastructures, and governance, while addressing data protection concerns. These measures must be flanked by further actions to foster data sharing skills and knowledge as well as, most importantly, the provision of appropriate incentives. Nevertheless, the proposed changes would be a breakthrough for clinical study data sharing in Germany, removing barriers regarding infrastructures, awareness, legal uncertainty, and responsibilities.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"140"},"PeriodicalIF":3.9,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12103796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141338","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":"\"Non-Markovian\" and \"directional\" errors inhibit scientific self-correction and can lead fields of study astray: an illustration using gardening and obesity-related outcomes.","authors":"Jon Agley, Sarah E Deemer, David B Allison","doi":"10.1186/s12874-025-02590-6","DOIUrl":"10.1186/s12874-025-02590-6","url":null,"abstract":"<p><p>Herein we coin the terms non-Markovian error and biased Markovian error to describe categories of scientific errors for which general progress within a field of study may be unlikely to result in scientific self-correction. We provide examples of such errors and show their impact on studies about gardening and obesity.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"137"},"PeriodicalIF":3.9,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141246","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":"Reviewing methodological approaches to dose-response modelling in complex interventions: insights and perspectives.","authors":"Mollie Payne, Dominic Stringer, Ben Carter, Amy Hardy, Richard Emsley","doi":"10.1186/s12874-025-02585-3","DOIUrl":"10.1186/s12874-025-02585-3","url":null,"abstract":"<p><strong>Background: </strong>Understanding dose-response relationships is crucial in optimizing clinical outcomes, particularly in complex interventions such as psychotherapy. While dose-response research is common in pharmaceutical contexts, its application in complex interventions remains underexplored. This review examines existing statistical methods for modelling dose-response relationships in complex interventions, focusing on psychotherapy.</p><p><strong>Methods: </strong>A systematic literature search following PRISMA guidelines identified studies proposing novel statistical methods or innovative applications of methods for analysing dose-response relationships. The search encompassed various databases, yielding 224 articles. After screening and exclusion, seven studies were eligible for analysis. Data synthesis categorized methods into three groups: multilevel and longitudinal modelling, non-parametric regression, and causal inference with instrumental variables. Additionally, a survey was conducted among clinical researchers to understand their perspectives on dosing decisions in psychotherapy trials.</p><p><strong>Results: </strong>Multilevel and longitudinal modelling techniques, although informative, were only applicable to participants with sessional data, limiting causal interpretations. Non-parametric regression methods provided avenues for causal inference but were constrained by assumptions. Causal inference with instrumental variables showed promise in addressing these limitations, particularly in randomised controlled trials, yet still require a priori assumption of the dose-response function. The results of our survey suggested that there is not sufficient information available to clinical researchers to make empirical dosing decisions in psychotherapeutic complex interventions.</p><p><strong>Conclusions: </strong>This review highlights the scarcity of robust statistical methods for evaluating dose-response relationships in psychotherapy trials. The dose-response methodology applied to RCTs remains underdeveloped, hindering causal interpretations or requiring strong assumptions. Traditional approaches oversimplify outcomes, highlighting the need for more sophisticated methodologies. Clinical researchers emphasized the necessity for clearer guidelines and enhanced patient involvement in dosing decisions, echoing the broader findings of the review. Future research requires methodological advancements to inform effective decision-making in psychotherapy trials, ultimately optimizing patient care and outcomes.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"135"},"PeriodicalIF":3.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085850","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":"Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users.","authors":"Christopher Meaney, Xuesong Wang, Jun Guan, Therese A Stukel","doi":"10.1186/s12874-025-02561-x","DOIUrl":"10.1186/s12874-025-02561-x","url":null,"abstract":"<p><strong>Background: </strong>Supervised machine learning is increasingly being used to estimate clinical predictive models. Several supervised machine learning models involve hyper-parameters, whose values must be judiciously specified to ensure adequate predictive performance.</p><p><strong>Objective: </strong>To compare several (nine) hyper-parameter optimization (HPO) methods, for tuning the hyper-parameters of an extreme gradient boosting model, with application to predicting high-need high-cost health care users.</p><p><strong>Methods: </strong>Extreme gradient boosting models were estimated using a randomly sampled training dataset. Models were separately trained using nine different HPO methods: 1) random sampling, 2) simulated annealing, 3) quasi-Monte Carlo sampling, 4-5) two variations of Bayesian hyper-parameter optimization via tree-Parzen estimation, 6-7) two implementations of Bayesian hyper-parameter optimization via Gaussian processes, 8) Bayesian hyper-parameter optimization via random forests, and 9) the covariance matrix adaptation evolutionary strategy. For each HPO method, we estimated 100 extreme gradient boosting models at different hyper-parameter configurations; and evaluated model performance using an AUC metric on a randomly sampled validation dataset. Using the best model identified by each HPO method, we evaluated generalization performance in terms of discrimination and calibration metrics on a randomly sampled held-out test dataset (internal validation) and a temporally independent dataset (external validation).</p><p><strong>Results: </strong>The extreme gradient boosting model estimated using default hyper-parameter settings had reasonable discrimination (AUC=0.82) but was not well calibrated. Hyper-parameter tuning using any HPO algorithm/sampler improved model discrimination (AUC=0.84), resulted in models with near perfect calibration, and consistently identified features predictive of high-need high-cost health care users.</p><p><strong>Conclusions: </strong>In our study, all HPO algorithms resulted in similar gains in model performance relative to baseline models. This finding likely relates to our study dataset having a large sample size, a relatively small number of features, and a strong signal to noise ratio; and would likely apply to other datasets with similar characteristics.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"134"},"PeriodicalIF":3.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075987","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":"Warnings on the inclusion of cluster randomized trials in meta-analysis: results of a simulation study.","authors":"Joseph Alvin Ramos Santos, Emilia Riggi, Gian Luca Di Tanna","doi":"10.1186/s12874-025-02586-2","DOIUrl":"10.1186/s12874-025-02586-2","url":null,"abstract":"<p><strong>Background: </strong>Consolidation of treatment effects from randomized controlled trials (RCT) is considered one of the highest forms of evidence in research. Cluster randomized trials (CRT) are increasingly used in the assessment of the effectiveness of interventions when individual-level randomization is impractical. In meta-analyses, CRTs that address the same clinical question as RCTs can be pooled in the same analysis; however, they need to be analyzed with appropriate statistical methods. This study examined the extent to which meta-analysis results are influenced by the inclusion of incorrectly analyzed CRTs through a series of simulations.</p><p><strong>Methods: </strong>RCT and CRT datasets were generated with a continuous treatment effect of zero, two trial arms, and equal number of participants. CRT datasets were generated with varying number of clusters (10, 20 or 40), observations per cluster (10, 30 or 50), total variance (1, 5 or 10) and ICC (0.05, 0.10 or 0.20). Each simulated CRT dataset (n = 1000 for each scenario) was analyzed using standard linear regression and mixed-effects regression with clusters treated as random effects to represent the incorrectly and correctly analyzed CRTs. Meta-analytic datasets were created by varying the total number of studies (4, 8 or 12), number of CRTs out of the total number of studies (single, half or all), and the number of correctly analyzed CRTs (none, half or all). Model performance was summarized from 1000 random-effects meta-analyses for each scenario.</p><p><strong>Results: </strong>The percentage of statistically significant results (at p < 0.05) was consistently lower when all CRTs were correctly analyzed. The alpha threshold (5%) was exceeded in 6 (2.47%) of 243 scenarios when all CRTs were correctly analyzed, compared to 177 (72.84%) and 195 (80.25%) scenarios when half or none of the CRTs were correctly analyzed, respectively. Coverage probabilities and model-based SEs were higher when all CRTs were correctly analyzed, while the estimated effect sizes and bias averaged across iterations showed no differences regardless of the number of correctly analyzed CRTs.</p><p><strong>Conclusions: </strong>Ignoring clustering in CRTs lead to inflated false-positive conclusions about the efficacy of treatments, highlighting the need for caution and proper analytical methods when incorporating CRTs into meta-analyses.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"133"},"PeriodicalIF":3.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12079878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144076017","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":"Incorporating latent survival trajectories and covariate heterogeneity in time-to-event data analysis: a joint mixture model approach.","authors":"Fu-Wen Liang, Wenyaw Chan, Michael D Swartz, Bouthaina S Dabaja","doi":"10.1186/s12874-025-02580-8","DOIUrl":"https://doi.org/10.1186/s12874-025-02580-8","url":null,"abstract":"<p><strong>Background: </strong>Finite mixture models have been recently applied in time-to-event data to identify subgroups with distinct hazard functions, yet they often assume differing covariate effects on failure times across latent classes but homogeneous covariate distributions. This study aimed to develop a method for analyzing time-to-event data while accounting for unobserved heterogeneity within a mixture modeling framework.</p><p><strong>Methods: </strong>A joint model was developed to incorporate latent survival trajectories and observed information for the joint analysis of time-to-event outcomes, correlated discrete and continuous covariates, and a latent class variable. It assumed covariate effects on survival times and covariate distributions vary across latent classes. Unobservable trajectories were identified by estimating the probability of belonging to a particular class based on observed information. This method was applied to a Hodgkin lymphoma study, identifying four distinct classes in terms of long-term survival and distributions of prognostic factors.</p><p><strong>Results: </strong>Results from simulation studies and the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. Four unobserved subgroups were identified, each characterized by distinct survival parameters and varying distributions of prognostic factors. A notable decreasing trend in the incidence of second malignancy over time was noted, along with different effects of second malignancy and relapse on survival across subgroups, providing deeper insights into disease progression over time.</p><p><strong>Conclusions: </strong>The proposed joint model effectively identifies latent subgroups, revealing unobserved heterogeneity in survival outcomes and prognostic factors. Its flexibility enables more precise estimation of survival trajectories, with broad applicability in survival analysis.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"132"},"PeriodicalIF":3.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12079916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144076013","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":"Inference for the treatment effect in staircase designs with continuous outcomes: a simulation study.","authors":"Ehsan Rezaei-Darzi, Kelsey L Grantham, Andrew B Forbes, Jessica Kasza","doi":"10.1186/s12874-025-02567-5","DOIUrl":"https://doi.org/10.1186/s12874-025-02567-5","url":null,"abstract":"<p><strong>Background: </strong>Staircase designs are incomplete stepped wedge designs that, unlike standard stepped wedge designs, require clusters to contribute data for only a limited number of trial periods. Previous work has provided formulae based on asymptotic results for the calculation of the power of staircase designs to detect treatment effects of interest.</p><p><strong>Methods: </strong>We conduct a simulation study to assess the finite sample performance of these formulae, and the impact of misspecifying the correlation structure when analysing data from staircase designs on inference for the treatment effect, under a range of realistic trial settings. This study focuses on basic staircase designs with one control period followed by one intervention period in each sequence. We simulate staircase trial datasets with continuous outcomes and a repeated cross-sectional measurement scheme under exchangeable and block-exchangeable intracluster correlation structures, and then fit linear mixed models with linear and categorical time period effects. For settings with a small number of clusters, Kenward-Roger and Satterthwaite small-sample corrections are applied. Comparisons are made between nominal and observed Type I error rates, and theoretically-derived study power and empirical power. The impact on inference for the treatment effect when misspecifying the intracluster correlation structure is assessed through considering performance metrics including bias and 95% confidence interval coverage.</p><p><strong>Results: </strong>Data analysis assuming an exchangeable correlation structure and application of the Satterthwaite correction controls Type I error well when the correlation structure is correctly specified, and there are a sufficient number of clusters. For the true block-exchangeable model, when fitting the correct model with the Satterthwaite correction, the observed Type I error (empirical power) can be higher (lower) than the nominal (i.e., theoretical) value when there is only 1 cluster per sequence, but otherwise, it aligns well with the nominal (theoretical) value. Misspecification of the correlation structure (fitting an exchangeable model when the true structure is block-exchangeable) can lead to inflated Type I error and poor confidence interval coverage.</p><p><strong>Conclusions: </strong>Staircase designs with one cluster per sequence should be used with caution. Additionally, using a correlation structure that allows for decay is preferable for making valid inferences for the estimation of the treatment effect.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"127"},"PeriodicalIF":3.9,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143973876","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":"A new framework for disentangling different components of excess mortality applied to Dutch care home residents during Covid-19.","authors":"Marije H Sluiskes, Eva A S Koster, Jelle J Goeman, Mar Rodríguez-Girondo, Hein Putter, Liesbeth C de Wreede","doi":"10.1186/s12874-025-02579-1","DOIUrl":"https://doi.org/10.1186/s12874-025-02579-1","url":null,"abstract":"<p><strong>Background: </strong>Vulnerable subgroups of the population, such as care home residents, often face elevated mortality risks during crises like pandemics or wars. To correctly model and interpret the excess mortality of vulnerable groups during crises, a distinction must be made between the pre-existing heightened mortality of the vulnerable group, the general population's excess mortality during the crisis, and the crisis-specific excess mortality unique to the vulnerable group.</p><p><strong>Methods: </strong>We introduce the concept of \"excess excess\" mortality, which captures the extra excess mortality experienced by vulnerable groups during crises, beyond what can be explained by their excess mortality due to being vulnerable and general population excess mortality. Using individual-level data from Statistics Netherlands, we model the excess excess mortality of Dutch care home residents aged 70 and older during the Covid-19 pandemic. We extend standard relative survival methods by incorporating multiple excess mortality components and use an additive hazards model to accommodate periods of negative excess hazard.</p><p><strong>Results: </strong>The findings confirm the severe impact of the Covid-19 pandemic on care home residents. In general, men and older age groups experienced higher excess excess mortality, both in absolute and relative terms.</p><p><strong>Conclusions: </strong>Our approach offers a new perspective on how to model and interpret excess mortality in vulnerable groups during a crisis and provides a methodological foundation for investigating excess excess mortality in other contexts.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"126"},"PeriodicalIF":3.9,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143953906","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}