BMC Medical Research Methodology最新文献

{"title":"Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial.","authors":"Lu Mao","doi":"10.1186/s12874-025-02554-w","DOIUrl":"https://doi.org/10.1186/s12874-025-02554-w","url":null,"abstract":"<p><strong>Background: </strong>The win ratio has been widely used in the analysis of hierarchical composite endpoints, which prioritize critical outcomes such as mortality over nonfatal, secondary events. Although a regression framework exists to incorporate covariates, it is limited to low-dimensional datasets and may struggle with numerous predictors. This gap necessitates a robust variable selection method tailored to the win ratio framework.</p><p><strong>Methods: </strong>We propose an elastic net-type regularization approach for win ratio regression, extending the proportional win-fractions (PW) model in low-dimensional settings. The method addresses key challenges, including adapting pairwise comparisons to penalized regression, optimizing model selection through subject-level cross-validation, and defining performance metrics via a generalized concordance index. The procedures are implemented in the wrnet R-package, publicly available at https://lmaowisc.github.io/wrnet/ .</p><p><strong>Results: </strong>Simulation studies demonstrate that wrnet outperforms traditional (regularized) Cox regression for time-to-first-event analysis, particularly in scenarios with differing covariate effects on mortality and nonfatal events. When applied to data from the HF-ACTION trial, the method identified prognostic variables and achieved superior predictive accuracy compared to regularized Cox models, as measured by overall and component-specific concordance indices.</p><p><strong>Conclusion: </strong>The wrnet approach combines the interpretability and clinical relevance of the win ratio with the scalability and robustness of elastic net regularization. The accompanying R-package provides a user-friendly interface for routine application of the procedures, whenever appropriate. Future research could explore additional applications or refine the methodology to address non-proportionalities in win-loss risks and nonlinearities in covariate effects.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"102"},"PeriodicalIF":3.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143967929","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":"The conclusiveness of trial sequential analysis varies with estimation of between-study variance: a case study.","authors":"Enoch Kang, James S Hodges, Yu-Chieh Chuang, Jin-Hua Chen, Chiehfeng Chen","doi":"10.1186/s12874-025-02545-x","DOIUrl":"https://doi.org/10.1186/s12874-025-02545-x","url":null,"abstract":"<p><strong>Background: </strong>Trial sequential methods have been introduced to address issues related to increased likelihood of incorrectly rejecting the null hypothesis in meta-analyses due to repeated significance testing. Between-study variance (τ²) and its estimate ( <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ²) play a crucial role in both meta-analysis and trial sequential analysis with the random-effects model. Therefore, we investigated how different <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ² impact the results of and quantities used in trial sequential analysis.</p><p><strong>Methods: </strong>This case study was grounded in a Cochrane review that provides data for smaller (< 10 randomized clinical trials, RCTs) and larger (> 20 RCTs) meta-analyses. The review compared various outcomes between video-laryngoscopy and direct laryngoscopy for tracheal intubation, and we used outcomes including hypoxemia and failed intubation, stratified by difficulty, expertise, and obesity. We calculated odds ratios using inverse variance method with six estimators for τ², including DerSimonian-Laird, restricted maximum-likelihood, Paule-Mandel, maximum-likelihood, Sidik-Jonkman, and Hunter-Schmidt. Then we depicted the relationships between <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ² and quantities in trial sequential analysis including diversity, adjustment factor, required information size (RIS), and α-spending boundaries.</p><p><strong>Results: </strong>We found that diversity increases logarithmically with <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ², and that the adjustment factor, RIS, and α-spending boundaries increase linearly with <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ². Also, the conclusions of trial sequential analysis can differ depending on the estimator used for between-study variance.</p><p><strong>Conclusion: </strong>This study highlights the importance of <math><mover><mi>τ</mi> <mo>^</mo></mover> </math> ² in trial sequential analysis and underscores the need to align the meta-analysis and the trial sequential analysis by choosing estimators to avoid introducing biases and discrepancies in effect size estimates and uncertainty assessments.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"101"},"PeriodicalIF":3.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969578","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 coefficients in segmented regression for interrupted time series analyses.","authors":"Yongzhe Wang, Narissa J Nonzee, Haonan Zhang, Kimlin T Ashing, Gaole Song, Catherine M Crespi","doi":"10.1186/s12874-025-02556-8","DOIUrl":"https://doi.org/10.1186/s12874-025-02556-8","url":null,"abstract":"<p><strong>Background: </strong>Segmented regression, a common model for interrupted time series (ITS) analysis, primarily utilizes two equation parametrizations. Interpretations of coefficients vary between the two segmented regression parametrizations, leading to occasional user misinterpretations.</p><p><strong>Methods: </strong>To illustrate differences in coefficient interpretation between two common parametrizations of segmented regression in ITS analysis, we derived analytical results and present an illustration evaluating the impact of a smoking regulation policy in Italy using a publicly accessible dataset. Estimated coefficients and their standard errors were obtained using two commonly used parametrizations for segmented regression with continuous outcomes. We clarified coefficient interpretations and intervention effect calculations.</p><p><strong>Results: </strong>Our investigation revealed that both parametrizations represent the same model. However, due to differences in parametrization, the immediate effect of the intervention is estimated differently under the two approaches. The key difference lies in the interpretation of the coefficient related to the binary indicator for intervention implementation, impacting the calculation of the immediate effect.</p><p><strong>Conclusions: </strong>Two common parametrizations of segmented regression represent the same model but have different interpretations of a key coefficient. Researchers employing either parametrization should exercise caution when interpreting coefficients and calculating intervention effects.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"98"},"PeriodicalIF":3.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143968600","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":"Power analysis for concurrent balanced or imbalanced multiple-intervention stepped wedge design: a simulation-based approach.","authors":"Yi Zhang, Meng Zheng, Xue-Zhi Liang, Qi Wang, Kun-Peng Wu, Ting-Ting Guo, Wen Chen","doi":"10.1186/s12874-025-02546-w","DOIUrl":"https://doi.org/10.1186/s12874-025-02546-w","url":null,"abstract":"<p><strong>Background: </strong>The concurrent multiple-intervention stepped wedge design (M-SWD) is one of the most widely used variants of the SWD. We aimed to conduct power analysis for concurrent balanced (equal number of clusters in intervention groups) and imbalanced (unequal number of clusters in intervention groups) M-SWDs.</p><p><strong>Methods: </strong>We conducted power analysis using a simulation-based approach with cross-sectional or closed-cohort designs and examined impact of design parameters (cluster size and number of clusters) and correlation parameters (total random effects variance (TRE), cluster autocorrelation coefficient (CAC), and individual autocorrelation coefficient (IAC)) on the powers of statistical tests for treatment effects.</p><p><strong>Results: </strong>With a fixed total sample size, increasing the number of clusters improves statistical power. When two treatment effects differ greatly, the concurrent imbalanced M-SWD saves sample size compared to the balanced design and powers could achieve the target value when the ratio of clusters approximates the inverse ratio of two effects. However, the allocation ratio should be no greater than 4:1. Additionally, statistical powers increased with decreasing TRE and increasing CAC and IAC. The impact of autocorrelation coefficients on powers is more pronounced when these parameters are large.</p><p><strong>Conclusion: </strong>When two treatment effects differ greatly, the concurrent imbalanced M-SWD, with an allocation ratio no larger than 4:1, is a preferred design over the balanced one. For both concurrent balanced and imbalanced M-SWD, it is recommended to set large number of clusters with small cluster sizes and to carefully consider estimates of correlation parameters when designing the trial.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"96"},"PeriodicalIF":3.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969391","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":"Joint modeling of multistate survival processes with informative examination scheme: application to progressions in diabetes.","authors":"Yuxi Zhu, Joshua J Joseph, Neena Thomas, Lang Li, Guy Brock","doi":"10.1186/s12874-025-02543-z","DOIUrl":"https://doi.org/10.1186/s12874-025-02543-z","url":null,"abstract":"<p><strong>Background: </strong>Multistate survival models (MSMs) are widely used in the medical field of clinical studies. For example, in type 2 diabetes mellitus (T2D), these models can be applied to describe progression in T2D by predefining several T2D states based on available biometric measurements such as hemoglobin A1 C (HbA1c). In most cases, MSMs come with an assumption that the examination process is independent of disease progression. However, in practice, complete independence between disease progression and examination processes is unrealistic, as the frequency at which a patient accesses healthcare may vary based on treatment and/or control of the health condition.</p><p><strong>Methods: </strong>We built a joint model of a 4-state transition process of T2D with informative examination scheme (i.e., the patterns of examination times are not random). Risk factors including age, sex, race, and socioeconomic disadvantage were included in a log-linear model examining T2D transition intensities and healthcare visit frequencies. Parameters of the joint model are estimated under the framework of likelihood function by the expectation-maximization (EM) algorithm.</p><p><strong>Results: </strong>The joint model demonstrated that people living in neighborhoods with greater socioeconomic disadvantage had a lower healthcare visit frequency under all 4 defined T2D statuses. Evaluation of race/ethnicity revealed that comparing to non-Hispanic White patients, Black patients had higher risk for progressing from Normal to Prediabetes, T2D, and Uncontrolled T2D states.</p><p><strong>Conclusions: </strong>Our joint model offers a framework for analyzing multistate survival processes while accounting for the dependence between disease progression and examination frequency. Unlike traditional MSMs that estimate only transition intensities, our model captures variations in healthcare visit frequencies across different disease states, providing a more comprehensive understanding of disease dynamics and healthcare access patterns.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"97"},"PeriodicalIF":3.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143976511","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":"Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records.","authors":"Dérick G F Borges, Eluã R Coutinho, Thiago Cerqueira-Silva, Malú Grave, Adriano O Vasconcelos, Luiz Landau, Alvaro L G A Coutinho, Pablo Ivan P Ramos, Manoel Barral-Netto, Suani T R Pinho, Marcos E Barreto, Roberto F S Andrade","doi":"10.1186/s12874-025-02542-0","DOIUrl":"https://doi.org/10.1186/s12874-025-02542-0","url":null,"abstract":"<p><strong>Background: </strong>Methods that enable early outbreak detection represent powerful tools in epidemiological surveillance, allowing adequate planning and timely response to disease surges. Syndromic surveillance data collected from primary healthcare encounters can be used as a proxy for the incidence of confirmed cases of respiratory diseases. Deviations from historical trends in encounter numbers can provide valuable insights into emerging diseases with the potential to trigger widespread outbreaks.</p><p><strong>Methods: </strong>Unsupervised machine learning methods and dynamical systems concepts were combined into the Mixed Model of Artificial Intelligence and Next-Generation (MMAING) ensemble, which aims to detect early signs of outbreaks based on primary healthcare encounters. We used data from 27 Brazilian health regions, which cover 41% of the country's territory, from 2017-2023 to identify anomalous increases in primary healthcare encounters that could be associated with an epidemic onset. Our validation approach comprised (i) a comparative analysis across Brazilian capitals; (ii) an analysis of warning signs for the COVID-19 period; and (iii) a comparison with related surveillance methods (namely EARS C1, C2, C3) based on real and synthetic labeled data.</p><p><strong>Results: </strong>The MMAING ensemble demonstrated its effectiveness in early outbreak detection using both actual and synthetic data, outperforming other surveillance methods. It successfully detected early warning signals in synthetic data, achieving a probability of detection of 86%, a positive predictive value of 85%, and an average reliability of 79%. When compared to EARS C1, C2, and C3, it exhibited superior performance based on receiver operating characteristic (ROC) curve results on synthetic data. When evaluated on real-world data, MMAING performed on par with EARS C2. Notably, the MMAING ensemble accurately predicted the onset of the four waves of the COVID-19 period in Brazil, further validating its effectiveness in real-world scenarios.</p><p><strong>Conclusion: </strong>Identifying trends in time series data related to primary healthcare encounters indicated the possibility of developing a reliable method for the early detection of outbreaks. MMAING demonstrated consistent identification capabilities across various scenarios, outperforming established reference methods.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"99"},"PeriodicalIF":3.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143971718","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":"Exploring the use of the aboriginal and Torres Strait Islander quality appraisal tool in Indigenous health research.","authors":"Samara Wessel, Kienan Williams, Mandi Gray, Sean M Bagshaw, Samantha L Bowker, Sarah A Elliott, Letebrhan Ferrow, Rita I Henderson, Kassandra Loewen, Deborah A McNeil, Auriele Volk, Jennifer Walker, Richard T Oster","doi":"10.1186/s12874-025-02539-9","DOIUrl":"https://doi.org/10.1186/s12874-025-02539-9","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"94"},"PeriodicalIF":3.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143980422","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":"Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies.","authors":"Hiroki Matsui, Kiyohide Fushimi, Hideo Yasunaga","doi":"10.1186/s12874-025-02549-7","DOIUrl":"https://doi.org/10.1186/s12874-025-02549-7","url":null,"abstract":"<p><strong>Background: </strong>Unmeasured confounders pose challenges when observational data are analysed in comparative effectiveness studies. Integrating high-dimensional administrative claims data may help adjust for unmeasured confounders. We determined whether distributed representations can compress high-dimensional administrative claims data to adjust for unmeasured confounders.</p><p><strong>Method: </strong>Using the Japanese Diagnosis Procedure Combination (DPC) database from 1291 hospitals (between April 2018 and March 2020), we applied the word2vec algorithm to create distributed representations for all medical codes. We focused on patients with heart failure (HF) and simulated four risk-adjustment models: 1, no adjustment; 2, adjusting for previously reported confounders; 3, adjusting for the sum of distributed representation weights of administrative claims data on the day of hospitalisation (novel method); and 4, a combination of models 2 and 3. We re-evaluated a previous study on the effect of early rehabilitation in patients with HF and compared these risk-adjustment methods (models 1-4).</p><p><strong>Results: </strong>Distributed representations were generated from the data of 15 998 963 in-patients, and 319 581 HF patients were identified. In the simulation study, Model 3 reduced the impact of unmeasured confounders and achieved better covariate balances than Model 1. Model 4 showed no increase in bias compared with the true model (Model 2) and was used as a reference model in the real-world application. When applied to a previous study, models 3 and 4 showed similar results.</p><p><strong>Conclusion: </strong>Distributed representation can compress detailed administrative claims data and adjust for unmeasured confounders in comparative effectiveness studies.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"95"},"PeriodicalIF":3.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969357","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":"Review of the Aboriginal and Torres Strait Islander Quality Appraisal Tool in Indigenous settings outside of Australia.","authors":"Stephen Harfield, Odette Pearson, Kim Morey, Karen Glover, Karla Canuto","doi":"10.1186/s12874-025-02538-w","DOIUrl":"https://doi.org/10.1186/s12874-025-02538-w","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"93"},"PeriodicalIF":3.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965076","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":"OpenClustered: an R package with a benchmark suite of clustered datasets for methodological evaluation and comparison.","authors":"Nathaniel Sean O'Connell, Jaime Lynn Speiser","doi":"10.1186/s12874-025-02548-8","DOIUrl":"https://doi.org/10.1186/s12874-025-02548-8","url":null,"abstract":"<p><strong>Background: </strong>Clustered data arise when observations are correlated within a group or sampling unit and frequently arise in epidemiology, social sciences, education, linguistics, econometrics, and medicine. Given growing interest in clustered data, we developed a data repository offering clustered datasets that can be used for methodologic comparison with open-source, publicly available data. Traditionally, data simulation studies are employed for methodology evaluation and comparison, which can be fraught with issues such as overly simplistic design and potential for bias. Excellent data repositories are available for standard (non-clustered) datasets, such as OpenML and the Penn Machine Learning Benchmark repository, but there is a paucity of resources available that have clustered data.</p><p><strong>Results: </strong>In this pilot study, we developed an R package called OpenClustered, which includes 19 clustered datasets with binary outcomes arising from various domains and varying in terms of their size and composition. We present tutorials for using OpenClustered, including examples for filtering and summarizing the datasets. We demonstrate the use of OpenClustered with a small benchmarking study comparing Frequentist and Bayesian implementations of generalized linear mixed models. All code and data are contained on the OpenClustered GitHub page.</p><p><strong>Conclusion: </strong>The OpenClustered R package is the start of a useful data resource for conducting benchmarking studies with open-source clustered data. It facilitates empirical methodologic guidance that is less prone to bias compared to data simulation studies, thereby improving rigor across diverse research fields. In the future, we plan to add more datasets, particularly those with continuous outcomes, as well as functionality for users to submit their clustered datasets to be included in the repository.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"92"},"PeriodicalIF":3.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143978944","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}