BMC Medical Research Methodology最新文献

{"title":"Correction: The gap between statistical and clinical significance: time to pay attention to clinical relevance in patient-reported outcome measures of insomnia.","authors":"Zongshi Qin, Yidan Zhu, Dong-Dong Shi, Rumeng Chen, Sen Li, Jiani Wu","doi":"10.1186/s12874-024-02354-8","DOIUrl":"10.1186/s12874-024-02354-8","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"230"},"PeriodicalIF":3.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11451206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142375118","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":"Adaptive designs in clinical trials: a systematic review-part I.","authors":"Mohamed Ben-Eltriki, Aisha Rafiq, Arun Paul, Devashree Prabhu, Michael O S Afolabi, Robert Baslhaw, Christine J Neilson, Michelle Driedger, Salaheddin M Mahmud, Thierry Lacaze-Masmonteil, Susan Marlin, Martin Offringa, Nancy Butcher, Anna Heath, Lauren E Kelly","doi":"10.1186/s12874-024-02272-9","DOIUrl":"10.1186/s12874-024-02272-9","url":null,"abstract":"<p><strong>Background: </strong>Adaptive designs (ADs) are intended to make clinical trials more flexible, offering efficiency and potentially cost-saving benefits. Despite a large number of statistical methods in the literature on different adaptations to trials, the characteristics, advantages and limitations of such designs remain unfamiliar to large parts of the clinical and research community. This systematic review provides an overview of the use of ADs in published clinical trials (Part I). A follow-up (Part II) will compare the application of AD in trials in adult and pediatric studies, to provide real-world examples and recommendations for the child health community.</p><p><strong>Methods: </strong>Published studies from 2010 to April 2020 were searched in the following databases: MEDLINE (Ovid), Embase (Ovid), and International Pharmaceutical Abstracts (Ovid). Clinical trial protocols, reports, and a secondary analyses using AD were included. We excluded trial registrations and interventions other than drugs or vaccines to align with regulatory guidance. Data from the published literature on study characteristics, types of adaptations, statistical analysis, stopping boundaries, logistical challenges, operational considerations and ethical considerations were extracted and summarized herein.</p><p><strong>Results: </strong>Out of 23,886 retrieved studies, 317 publications of adaptive trials, 267 (84.2%) trial reports, and 50 (15.8%) study protocols), were included. The most frequent disease was oncology (168/317, 53%). Most trials included only adult participants (265, 83.9%),16 trials (5.4%) were limited to only children and 28 (8.9%) were for both children and adults, 8 trials did not report the ages of the included populations. Some studies reported using more than one adaptation (there were 390 reported adaptations in 317 clinical trial reports). Most trials were early in drug development (phase I, II (276/317, 87%). Dose-finding designs were used in the highest proportion of the included trials (121/317, 38.2 %). Adaptive randomization (53/317, 16.7%), with drop-the-losers (or pick-the-winner) designs specifically reported in 29 trials (9.1%) and seamless phase 2-3 design was reported in 27 trials (8.5%). Continual reassessment methods (60/317, 18.9%) and group sequential design (47/317, 14.8%) were also reported. Approximately two-thirds of trials used frequentist statistical methods (203/309, 64%), while Bayesian methods were reported in 24% (75/309) of included trials.</p><p><strong>Conclusion: </strong>This review provides a comprehensive report of methodological features in adaptive clinical trials reported between 2010 and 2020. Adaptation details were not uniformly reported, creating limitations in interpretation and generalizability. Nevertheless, implementation of existing reporting guidelines on ADs and the development of novel educational strategies that address the scientific, operational challenges and ethical considera","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"229"},"PeriodicalIF":3.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11451232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142375117","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":"Prognostic score-based model averaging approach for propensity score estimation.","authors":"Daijiro Kabata, Elizabeth A Stuart, Ayumi Shintani","doi":"10.1186/s12874-024-02350-y","DOIUrl":"10.1186/s12874-024-02350-y","url":null,"abstract":"<p><strong>Background: </strong>Propensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis.</p><p><strong>Methods: </strong>We conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment.</p><p><strong>Results: </strong>The simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates.</p><p><strong>Discussion: </strong>The prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained u","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"228"},"PeriodicalIF":3.9,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11448247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142370961","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":"Three-outcome designs for external pilot trials with progression criteria.","authors":"Duncan T Wilson, Eleanor Hudson, Sarah Brown","doi":"10.1186/s12874-024-02351-x","DOIUrl":"10.1186/s12874-024-02351-x","url":null,"abstract":"<p><strong>Background: </strong>Whether or not to progress from a pilot study to a definitive trial is often guided by pre-specified quantitative progression criteria with three possible outcomes. Although the choice of these progression criteria will help to determine the statistical properties of the pilot trial, there is a lack of research examining how they, or the pilot sample size, should be determined.</p><p><strong>Methods: </strong>We review three-outcome trial designs originally proposed in the phase II oncology setting and extend these to the case of external pilots, proposing a unified framework based on univariate hypothesis tests and the control of frequentist error rates. We apply this framework to an example and compare against a simple two-outcome alternative.</p><p><strong>Results: </strong>We find that three-outcome designs can be used in the pilot setting, although they are not generally more efficient than simpler two-outcome alternatives. We show that three-outcome designs can help allow for other sources of information or other stakeholders to feed into progression decisions in the event of a borderline result, but this will come at the cost of a larger pilot sample size than the two-outcome case. We also show that three-outcome designs can be used to allow adjustments to be made to the intervention or trial design before commencing the definitive trial, providing the effect of the adjustment can be accurately predicted at the pilot design stage. An R package, tout, is provided to optimise progression criteria and pilot sample size.</p><p><strong>Conclusions: </strong>The proposed three-outcome framework provides a way to optimise pilot trial progression criteria and sample size in a way that leads to desired operating characteristics. It can be applied whether or not an adjustment following the pilot trial is anticipated, but will generally lead to larger sample size requirements than simpler two-outcome alternatives.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"226"},"PeriodicalIF":3.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364360","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":"Time to STEP UP: methods and findings from the development of guidance to help researchers design inclusive clinical trials.","authors":"K Biggs, K Hullock, C Dix, J A Lane, H Green, S Treweek, F Shiely, V Shepherd, A Willis, T Isaacs, C Cooper","doi":"10.1186/s12874-024-02342-y","DOIUrl":"10.1186/s12874-024-02342-y","url":null,"abstract":"<p><strong>Background: </strong>It is important to design clinical trials to include all those who may benefit from the intervention being tested. Several frameworks have been developed to help researchers think about the barriers to inclusion of particular under-served groups when designing a trial, but there is a lack of practical guidance on how to implement these frameworks. This paper describes the ACCESS project, the findings from each phase of the project and the guidance we developed (STEP UP) on how to design more inclusive trials.</p><p><strong>Methods: </strong>Development of the STEP UP guidance had five phases: (1) Scoping literature review, (2) 'roundtable' discussion meetings, (3) redesign of trials, (4) interviews and (5) guidance document development, with input from public contributors and the ACCESS team.</p><p><strong>Results: </strong>Over 40 experts contributed to the ACCESS project-patients and the public, clinicians, NHS research staff, trialists and other academics. The scoping review identified several strategies being used to improve inclusion, mostly around recruitment settings, but there was little evaluation of these strategies. The 'roundtable' discussions identified additional strategies being used across the UK and Ireland to improve inclusion, which were grouped into: Communication, Community engagement, Recruitment sites, Patient information, Flexibility, Recruitment settings, Consent process, Monitoring, Training for researchers and Incentives. These strategies were used to redesign three existing trials by applying one of the three INCLUDE frameworks (ethnicity, socioeconomic disadvantage, impaired capacity to consent) to one trial each, to produce the key recommendations for the guidance. Issues around implementation were explored in stakeholder interviews and key facilitators were identified: funders requesting information on inclusion, having the time and funding to implement strategies, dedicated staff, flexibility in trial protocols, and considering inclusion of under-served groups at the design stages. The STEP UP guidance is freely available at http://step-up-clinical-trials.co.uk .</p><p><strong>Conclusion: </strong>Researchers should consider inclusivity to shape initial trial design decisions. Trial teams and funders need to ensure that trials are given both the resources and time needed to implement the STEP UP guidance and increase the opportunities to recruit a diverse population.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"227"},"PeriodicalIF":3.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11445965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364361","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":"Bayesian spatial-temporal analysis and determinants of cardiovascular diseases in Tanzania mainland.","authors":"Bernada E Sianga, Maurice C Mbago, Amina S Msengwa","doi":"10.1186/s12874-024-02348-6","DOIUrl":"10.1186/s12874-024-02348-6","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular Diseases (CVDs) are health-threatening conditions that account for high mortality in the world. Approximately 23.6 million deaths due to CVD is expected in the year 2030 worldwide. The CVD burden is more severe in developing countries, including Tanzania.</p><p><strong>Objectives: </strong>This study analyzed the spatial-temporal trends and determinants of cardiovascular diseases in Tanzania from 2010 to 2019.</p><p><strong>Methods: </strong>Individual data were extracted from Jakaya Kikwete Cardiac Institute (JKCI), Mbeya Zonal Referral Hospital (MZRH), Kilimanjaro Christian Medical Centre (KCMC) and Bugando hospitals and the geographical data from TMA. The model containing spatial and temporal components was analyzed using the Bayesian hierarchical method implemented using Integrated Nested Laplace Approximation (INLA).</p><p><strong>Results: </strong>The results found that the incidence of CVD increased from 2010 to 2014 and decreased from 2015 to 2019. The southern highlands, lake, central and coastal zones were more likely to have CVD problems than others. It was also revealed that people aged 60-64 years OR = 1.49, females OR = 1.51, smokers OR = 1.76, alcohol drinkers OR = 1.48, and overweight OR = 1.89 were more likely to have CVD problems. Additionally, a 1^oC increase in the average annual air maximum temperature was related to a 14% risk of developing CVD problems. The study revealed that the model, which included spatial and temporal random effects, was the best-predicting model.</p><p><strong>Conclusion: </strong>The study shows a decreased CVD incidence rate from 2015 to 2019. The CVD incidences occurred more in Tanzania's coastal and lake areas between 2010 and 2019. The demographic, lifestyle and geographical risk factors were significantly associated with the CVD.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"225"},"PeriodicalIF":3.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11445964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364359","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":"FOUNTAIN: a modular research platform for integrated real-world evidence generation.","authors":"Nikolaus G Oberprieler, Manel Pladevall-Vila, Catherine Johannes, J Bradley Layton, Asieh Golozar, Martin Lavallee, Fangfang Liu, Maria Kubin, David Vizcaya","doi":"10.1186/s12874-024-02344-w","DOIUrl":"10.1186/s12874-024-02344-w","url":null,"abstract":"<p><strong>Background: </strong>Real-world evidence (RWE) plays a key role in regulatory and healthcare decision-making, but the potentially fragmentated nature of generated evidence may limit its utility for clinical decision-making. Heterogeneity and a lack of reproducibility in RWE resulting from inconsistent application of methodologies across data sources should be minimized through harmonization.</p><p><strong>Methods: </strong>This paper's aim is to describe and reflect upon a multidisciplinary research platform (FOUNTAIN; FinerenOne mUlti-database NeTwork for evidence generAtIoN) with coordinated studies using diverse RWE generation approaches and explore the platform's strengths and limitations. With guidance from an executive advisory committee of multidisciplinary experts and patient representatives, the goal of the FOUNTAIN platform is to harmonize RWE generation across a portfolio of research projects, including research partner collaborations and a common data model (CDM)-based program. FOUNTAIN's overarching objectives as a research platform are to establish long-term collaborations among pharmacoepidemiology research partners and experts and to integrate diverse approaches for RWE generation, including global protocol execution by research partners in local data sources and common protocol execution in multiple data sources through federated data networks, while ensuring harmonization of medical definitions, methodology, and reproducible artifacts across all studies. Specifically, the aim of the multiple studies run within the frame of FOUNTAIN is to provide insight into the real-world utilization, effectiveness, and safety of finerenone across its life-cycle.</p><p><strong>Results: </strong>Currently, the FOUNTAIN platform includes 9 research partner collaborations and 8 CDM-mapped data sources from 7 countries (United States, United Kingdom, China, Japan, The Netherlands, Spain, and Denmark). These databases and research partners were selected after a feasibility fit-for-purpose evaluation. Six multicountry, multidatabase, cohort studies are ongoing to describe patient populations, current standard of care, comorbidity profiles, healthcare resource use, and treatment effectiveness and safety in different patient populations with chronic kidney disease and type 2 diabetes. Strengths and potential limitations of FOUNTAIN are described in the context of valid RWE generation.</p><p><strong>Conclusion: </strong>The establishment of the FOUNTAIN platform has allowed harmonized execution of multiple studies, promoting consistency both within individual studies that employ multiple data sources and across all studies run within the platform's framework. FOUNTAIN presents a proposal to efficiently improve the consistency and generalizability of RWE on finerenone.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"224"},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11445988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142361000","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":"Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model.","authors":"Agus Salim, Christian J Brakenridge, Dulari Hakamuwa Lekamlage, Erin Howden, Ruth Grigg, Hayley T Dillon, Howard D Bondell, Julie A Simpson, Genevieve N Healy, Neville Owen, David W Dunstan, Elisabeth A H Winkler","doi":"10.1186/s12874-024-02311-5","DOIUrl":"10.1186/s12874-024-02311-5","url":null,"abstract":"<p><strong>Background: </strong>Wrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts.</p><p><strong>Methods: </strong>We developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL.</p><p><strong>Results: </strong>In OPTIMISE cohort, STEPHEN's estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06-0.25] vs. 0.23 [0.13-0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06-0.26] vs. 0.42[0.32-0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03-0.25] vs. 0.36[0.15-0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes).</p><p><strong>Conclusions: </strong>STEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/ . The package includes trained models, but users have the flexibility to train their own models.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"222"},"PeriodicalIF":3.9,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11440759/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341473","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 impact of allocation bias on test decisions in clinical trials with multiple endpoints using multiple testing strategies.","authors":"Stefanie Schoenen, Nicole Heussen, Johan Verbeeck, Ralf-Dieter Hilgers","doi":"10.1186/s12874-024-02335-x","DOIUrl":"10.1186/s12874-024-02335-x","url":null,"abstract":"<p><strong>Background: </strong>Considering multiple endpoints in clinical trials provide a more comprehensive understanding of treatment effects and may lead to increased power or reduced sample size, which may be beneficial in rare diseases. Besides the small sample sizes, allocation bias is an issue that affects the validity of these trials. We investigate the impact of allocation bias on testing decisions in clinical trials with multiple endpoints and offer a tool for selecting an appropriate randomization procedure (RP).</p><p><strong>Methods: </strong>We derive a model for quantifying the effect of allocation bias depending on the RP in the case of two-arm parallel group trials with continuous multiple endpoints. We focus on two approaches to analyze multiple endpoints, either the Šidák procedure to show efficacy in at least one endpoint and the all-or-none procedure to show efficacy in all endpoints.</p><p><strong>Results: </strong>To evaluate the impact of allocation bias on the test decision we propose a biasing policy for multiple endpoints. The impact of allocation on the test decision is measured by the family-wise error rate of the Šidák procedure and the type I error rate of the all-or-none procedure. Using the biasing policy we derive formulas to calculate these error rates. In simulations we show that, for the Šidák procedure as well as for the all-or-none procedure, allocation bias leads to inflation of the mean family-wise error and mean type I error, respectively. The strength of this inflation is affected by the choice of the RP.</p><p><strong>Conclusion: </strong>Allocation bias should be considered during the design phase of a trial to increase validity. The developed methodology is useful for selecting an appropriate RP for a clinical trial with multiple endpoints to minimize allocation bias effects.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"223"},"PeriodicalIF":3.9,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11441119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341479","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":"Implementing multiple imputations for addressing missing data in multireader multicase design studies.","authors":"Zhemin Pan, Yingyi Qin, Wangyang Bai, Qian He, Xiaoping Yin, Jia He","doi":"10.1186/s12874-024-02321-3","DOIUrl":"https://doi.org/10.1186/s12874-024-02321-3","url":null,"abstract":"<p><strong>Background: </strong>In computer-aided diagnosis (CAD) studies utilizing multireader multicase (MRMC) designs, missing data might occur when there are instances of misinterpretation or oversight by the reader or problems with measurement techniques. Improper handling of these missing data can lead to bias. However, little research has been conducted on addressing the missing data issue within the MRMC framework.</p><p><strong>Methods: </strong>We introduced a novel approach that integrates multiple imputation with MRMC analysis (MI-MRMC). An elaborate simulation study was conducted to compare the efficacy of our proposed approach with that of the traditional complete case analysis strategy within the MRMC design. Furthermore, we applied these approaches to a real MRMC design CAD study on aneurysm detection via head and neck CT angiograms to further validate their practicality.</p><p><strong>Results: </strong>Compared with traditional complete case analysis, the simulation study demonstrated the MI-MRMC approach provides an almost unbiased estimate of diagnostic capability, alongside satisfactory performance in terms of statistical power and the type I error rate within the MRMC framework, even in small sample scenarios. In the real CAD study, the proposed MI-MRMC method further demonstrated strong performance in terms of both point estimates and confidence intervals compared with traditional complete case analysis.</p><p><strong>Conclusion: </strong>Within MRMC design settings, the adoption of an MI-MRMC approach in the face of missing data can facilitate the attainment of unbiased and robust estimates of diagnostic capability.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"217"},"PeriodicalIF":3.9,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11428558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341474","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}