BMC Medical Research Methodology最新文献

{"title":"When does adjusting covariate under randomization help? A comparative study on current practices.","authors":"Ying Gao, Yi Liu, Roland Matsouaka","doi":"10.1186/s12874-024-02375-3","DOIUrl":"10.1186/s12874-024-02375-3","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to thoroughly compare past and current methods that leverage baseline covariate information to estimate the average treatment effect (ATE) using data from of randomized clinical trials (RCTs). We especially focus on their performance, efficiency gain, and power.</p><p><strong>Methods: </strong>We compared 6 different methods using extensive Monte-Carlo simulation studies: the unadjusted estimator, i.e., analysis of variance (ANOVA), the analysis of covariance (ANCOVA), the analysis of heterogeneous covariance (ANHECOVA), the inverse probability weighting (IPW), the augmented inverse probability weighting (AIPW), and the overlap weighting (OW) as well as the augmented overlap weighting (AOW) estimators. The performance of these methods is assessed using the relative bias (RB), the root mean square error (RMSE), the model-based standard error (SE) estimation, the coverage probability (CP), and the statistical power.</p><p><strong>Results: </strong>Even with a well-executed randomization, adjusting for baseline covariates by an appropriate method can be a good practice. When the outcome model(s) used in a covariate-adjusted method is closer to the correctly specified model(s), the efficiency and power gained can be substantial. We also found that most covariate-adjusted methods can suffer from the high-dimensional curse, i.e., when the number of covariates is relatively high compared to the sample size, they can have poor performance (along with lower efficiency) in estimating ATE. Among the different methods we compared, the OW performs the best overall with smaller RMSEs and smaller model-based SEs, which also result in higher power when the true effect is non-zero. Furthermore, the OW is more robust when dealing with the high-dimensional issue.</p><p><strong>Conclusion: </strong>To effectively use covariate adjustment methods, understanding their nature is important for practical investigators. Our study shows that outcome model misspecification and high-dimension are two main burdens in a covariate adjustment method to gain higher efficiency and power. When these factors are appropriately considered, e.g., performing some variable selections if the data dimension is high before adjusting covariate, these methods are expected to be useful.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"250"},"PeriodicalIF":3.9,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495059","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":"Tracking modifications to implementation strategies: a case study from SNaP - a hybrid type III randomized controlled trial to scale up integrated systems navigation and psychosocial counseling for PWID with HIV in Vietnam.","authors":"Minh X Nguyen, Sophia M Bartels, Christopher F Akiba, Teerada Sripaipan, Ha Tt Nong, Linh Th Dang, Ha V Tran, Van Th Hoang, Giang M Le, Vivian F Go, William C Miller, Byron J Powell","doi":"10.1186/s12874-024-02367-3","DOIUrl":"10.1186/s12874-024-02367-3","url":null,"abstract":"<p><strong>Introduction: </strong>Evaluation of implementation strategies is core to implementation trials, but implementation strategies often deviate from the original plan to adjust to the real-world conditions. The optimal approach to track modifications to implementation strategies is unclear, especially in low-resource settings. Using data from an implementation trial for people who inject drugs (PWID) with HIV in Vietnam, we describe the tracking of implementation strategy modifications and present findings of this process.</p><p><strong>Methods: </strong>SNaP (Systems Navigation and Psychosocial Counseling) is a hybrid type-III effectiveness-implementation randomized controlled trial aiming to scale up the evidence-based intervention, integrated systems navigation and psychosocial counseling, for PWID with HIV in Vietnam. Forty-two HIV testing sites were randomized 1:1 to a standard or tailored arm. While the standard arm (SA) received a uniform package of strategies, implementation strategies for the tailored arm (TA) were tailored to address specific needs of each site. The central research team also met monthly with the TA to document how their tailored strategies were implemented over time. Five components were involved in the tracking process: describing the planned strategies; tracking strategy use; monitoring barriers and solutions; describing modifications; and identifying and describing any additional strategies.</p><p><strong>Results: </strong>Our approach allowed us to closely track the modifications to implementation strategies in the tailored arms every month. TA sites originally identified 27 implementation strategies prior to implementation. During implementation, five strategies were dropped by four sites and two new strategies were added to twelve sites. Modifications of five strategies occurred at four sites to accommodate their changing needs and resources. Difficulties related to the COVID-19 pandemic, low number of participants recruited, high workload at the clinic, lack of resources for HIV testing and high staff turnover were among barriers of implementing the strategies. A few challenges to tracking modifications were noted, including the considerable amount of time and efforts needed as well as the lack of motivation from site staff to track and keep written documentations of modifications.</p><p><strong>Conclusions: </strong>We demonstrated the feasibility of a systematic approach to tracking implementation strategies for a large-scale implementation trial in a low-resource setting. This process could be further enhanced and replicated in similar settings to balance the rigor and feasibility of implementation strategy tracking. Our findings can serve as additional guidelines for future researchers planning to report and track modifications to implementation strategies in large, complex trials.</p><p><strong>Trial registration: </strong>clinicaltrials.gov ID: NCT03952520 (first posted 2019-05-16).</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"249"},"PeriodicalIF":3.9,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495058","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":"Longitudinal mediation analysis with multilevel and latent growth models: a separable effects causal approach.","authors":"Chiara Di Maria, Vanessa Didelez","doi":"10.1186/s12874-024-02358-4","DOIUrl":"10.1186/s12874-024-02358-4","url":null,"abstract":"<p><strong>Background: </strong>Causal mediation analysis is widespread in applied medical research, especially in longitudinal settings. However, estimating natural mediational effects in such contexts is often difficult because of the presence of post-treatment confounding. Moreover, many models frequently used in applied research, like multilevel and latent growth models, present an additional difficulty, i.e. the presence of latent variables. In this paper, we propose a causal interpretation of these two classes of models based on a novel type of causal effects called separable, which overcome some of the issues of natural effects.</p><p><strong>Methods: </strong>We formally derive conditions for the identifiability of separable mediational effects and their analytical expressions based on the g-formula. We carry out a simulation study to investigate how moderate and severe model misspecification, as well as violation of the identfiability assumptions, affect estimates. We also present an application to real data.</p><p><strong>Results: </strong>The results show how model misspecification impacts the estimates of mediational effects, particularly in the case of severe misspecification, and that the bias worsens over time. The violation of assumptions affects separable effect estimates in a very different way for the mixed effect and the latent growth models.</p><p><strong>Conclusion: </strong>Our approach allows us to give multilevel and latent growth models an appealing causal interpretation based on separable effects. The simulation study shows that model misspecification can heavily impact effect estimates, highlighting the importance of careful model choice.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"248"},"PeriodicalIF":3.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11515317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495025","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":"LLpowershap: logistic loss-based automated Shapley values feature selection method.","authors":"Iqbal Madakkatel, Elina Hyppönen","doi":"10.1186/s12874-024-02370-8","DOIUrl":"10.1186/s12874-024-02370-8","url":null,"abstract":"<p><strong>Background: </strong>Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, feature selection methods using predictive Shapley values and p-values have been introduced, including powershap.</p><p><strong>Methods: </strong>We present a novel feature selection method, LLpowershap, that takes forward these recent advances by employing loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. We also enhance the calculation of p-values and power to identify informative features and to estimate number of iterations of model development and testing.</p><p><strong>Results: </strong>Our simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or comparable predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods. LLpowershap is also ranked the best in mean ranking among the seven feature selection methods tested on the benchmark datasets.</p><p><strong>Conclusion: </strong>Our results demonstrate that LLpowershap is a viable wrapper feature selection method that can be used for feature selection in large biomedical datasets and other settings.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"247"},"PeriodicalIF":3.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11515487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495024","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":"Analyzing missingness patterns in real-world data using the SMDI toolkit: application to a linked EHR-claims pharmacoepidemiology study.","authors":"Sudha R Raman, Bradley G Hammill, Pamela A Shaw, Hana Lee, Sengwee Toh, John G Connolly, Kimberly J Dandreo, Vinit Nalawade, Fang Tian, Wei Liu, Jie Li, José J Hernández-Muñoz, Robert J Glynn, Rishi J Desai, Janick Weberpals","doi":"10.1186/s12874-024-02330-2","DOIUrl":"10.1186/s12874-024-02330-2","url":null,"abstract":"<p><strong>Background: </strong>Missing data in confounding variables present a frequent challenge in generating evidence using real-world data, including electronic health records (EHR). Our objective was to apply a recently published toolkit for characterizing missing data patterns and based on the toolkit results about likely missingness mechanisms, illustrate the decision-making process for analyses in an empirical case example.</p><p><strong>Methods: </strong>We utilized the Structural Missing Data Investigations (SMDI) toolkit to characterize missing data patterns in the context of a pharmacoepidemiology study comparing cardiovascular outcomes of initiating sodium-glucose-cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP-4i) among older adults. The study used a linked EHR-Medicare claims dataset from Duke Health patients (2015-2017), focusing on partially observed confounders from EHR data (HbA1c lab and body mass index [BMI] values). Our analysis incorporated SMDI's descriptive functions and diagnostic tests to explore missingness patterns and determine missingness mitigation approaches. We used findings from these investigations to inform estimation of adjusted hazard ratios comparing the two classes of medications.</p><p><strong>Results: </strong>High levels of missingness were noted for important confounding variables including HbA1c (63.6%) and BMI (16.5%). Diagnostic tests resulted in output that described: 1) the distributions of patient characteristics, exposure, and outcome between patients with or without an observed value of the partially observed covariate, 2) the ability to predict missingness based on observed covariates, and 3) estimate if the missingness of a partially observed covariate is differential with respect to the outcome. There was evidence that missingness could be sufficiently described using observed data, which allowed multiple imputation by chained equations using random forests to address missing confounder data in estimating treatment effects. Multiple imputation resulted in improved alignment of effect estimates with previous studies.</p><p><strong>Conclusions: </strong>We were able to demonstrate the practical application of the SMDI toolkit in a real-world setting. Application of the SMDI toolkit and the resulting insights of potential missingness patterns can inform the choice of appropriate analytic methods and increase transparency of research methods in handling missing data. This type of approach can inform analytic decision making and may increase our ability to generate evidence from real-world data.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"246"},"PeriodicalIF":3.9,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11490010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457860","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":"Application of the estimand framework for an emulated trial using reference based multiple imputation to investigate informative censoring.","authors":"A Atkinson, M Zwahlen, S De Wit, H Furrer, J R Carpenter","doi":"10.1186/s12874-024-02364-6","DOIUrl":"https://doi.org/10.1186/s12874-024-02364-6","url":null,"abstract":"<p><strong>Background: </strong>The ICH E9 (R1) addendum on Estimands and Sensitivity analysis in Clinical trials proposes a framework for the design and analysis of clinical trials aimed at improving clarity around the definition of the targeted treatment effect (the estimand) of a study.</p><p><strong>Methods: </strong>We adopt the estimand framework in the context of a study using \"trial emulation\" to estimate the risk of pneumocystis pneumonia, an opportunistic disease contracted by people living with HIV and AIDS having a weakened immune system, when considering two antibiotic treatment regimes for stopping antibiotic prophylaxis treatment against this disease. A \"while on treatment\" strategy has been implemented for post-randomisation (intercurrent) events. We then perform a sensitivity analysis using reference based multiple imputation to model a scenario in which patients lost to follow-up stop taking prophylaxis.</p><p><strong>Results: </strong>The primary analysis indicated a protective effect for the new regime which used viral suppression as prophylaxis stopping criteria (hazard ratio (HR) 0.78, 95% confidence interval [0.69, 0.89], p < 0.001). For the sensitivity analysis, when we apply the \"jump to off prophylaxis\" approach, the hazard ratio is almost the same compared to that from the primary analysis (HR 0.80 [0.69, 0.95], p = 0.009). The sensitivity analysis confirmed that the new regime exhibits a clear improvement over the existing guidelines for PcP prophylaxis when those lost to follow-up \"jump to off prophylaxis\".</p><p><strong>Conclusions: </strong>Our application using reference based multiple imputation demonstrates the method's flexibility and simplicity for sensitivity analyses in the context of the estimand framework for (emulated) trials.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"245"},"PeriodicalIF":3.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457861","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":"Meta-analysis accelerator: a comprehensive tool for statistical data conversion in systematic reviews with meta-analysis.","authors":"Abdallah Abbas, Mahmoud Tarek Hefnawy, Ahmed Negida","doi":"10.1186/s12874-024-02356-6","DOIUrl":"10.1186/s12874-024-02356-6","url":null,"abstract":"<p><strong>Background: </strong>Systematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools.</p><p><strong>Implementation: </strong>Meta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, request a conversion feature, about page, articles page, and privacy policy page. This comprehensive design supports the tool's primary goal of simplifying the meta-analysis process.</p><p><strong>Results: </strong>Since its initial release in October 2023 as Meta Converter and subsequent renaming to Meta-Analysis Accelerator, the tool has gained widespread use globally. From March 2024 to May 2024, it received 12,236 visits from countries such as Egypt, France, Indonesia, and the USA, indicating its international appeal and utility. Approximately 46% of the visits were direct, reflecting its popularity and trust among users.</p><p><strong>Conclusions: </strong>Meta-Analysis Accelerator significantly enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. Its comprehensive variety of conversions, user-friendly interface, and continuous improvements make it an indispensable resource for researchers. The tool's ability to streamline data transformation ensures that researchers can focus more on data interpretation and less on manual calculations, thus advancing the quality and ease of conducting systematic reviews and meta-analyses.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"243"},"PeriodicalIF":3.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457874","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 study of implementation factors for a novel approach to clinical trials: constructs for consideration in the coordination of direct-to-patient online-based medical research.","authors":"Peter F Cronholm, Janelle Applequist, Jeffrey Krischer, Ebony Fontenot, Trocon Davis, Cristina Burroughs, Carol A McAlear, Renée Borchin, Joyce Kullman, Simon Carette, Nader Khalidi, Curry Koening, Carol A Langford, Paul Monach, Larry Moreland, Christian Pagnoux, Ulrich Specks, Antoine G Sreih, Steven R Ytterberg, Peter A Merkel","doi":"10.1186/s12874-024-02352-w","DOIUrl":"https://doi.org/10.1186/s12874-024-02352-w","url":null,"abstract":"<p><strong>Background: </strong>Traditional medical research infrastructures relying on the Centers of Excellence (CoE) model (an infrastructure or shared facility providing high standards of research excellence and resources to advance scientific knowledge) are often limited by geographic reach regarding patient accessibility, presenting challenges for study recruitment and accrual. Thus, the development of novel, patient-centered (PC) strategies (e.g., the use of online technologies) to support recruitment and streamline study procedures are necessary. This research focused on an implementation evaluation of a design innovation with implementation outcomes as communicated by study staff and patients for CoE and PC approaches for a randomized controlled trial (RCT) for patients with vasculitis.</p><p><strong>Methods: </strong>In-depth qualitative interviews were conducted with 32 individuals (17 study team members, 15 patients). Transcripts were coded using the Consolidated Framework for Implementation Research (CFIR).</p><p><strong>Results: </strong>The following CFIR elements emerged: characteristics of the intervention, inner setting, characteristics of individuals, and process. From the staff perspective, the communication of the PC approach was a major challenge, but should have been used as an opportunity to identify one \"point person\" in charge of all communicative elements among the study team. Study staff from both arms were highly supportive of the PC approach and saw its promise, particularly regarding online consent procedures. Patients reported high self-efficacy in reference to the PC approach and utilization of online technologies. Local physicians were integral for making patients feel comfortable about participation in research studies.</p><p><strong>Conclusions: </strong>The complexity of replicating the interpersonal nature of the CoE model in the virtual setting is substantial, meaning the PC approach should be viewed as a hybrid strategy that integrates online and face-to-face practices.</p><p><strong>Trial registrations: </strong>1) Name: The Assessment of Prednisone In Remission Trial - Centers of Excellence Approach (TAPIR).</p><p><strong>Trial registration number: </strong>ClinicalTrials.gov NCT01940094 . Date of registration: September 10, 2013. 2) Name: The Assessment of Prednisone In Remission Trial - Patient Centric Approach (TAPIR).</p><p><strong>Trial registration number: </strong>Clinical Trials.gov NCT01933724 . Date of registration: September 2, 2013.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"244"},"PeriodicalIF":3.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457857","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":"Two-stage group-sequential designs with delayed responses - what is the point of applying corresponding methods?","authors":"Stephen Schüürhuis, Gernot Wassmer, Meinhard Kieser, Friedrich Pahlke, Cornelia Ursula Kunz, Carolin Herrmann","doi":"10.1186/s12874-024-02363-7","DOIUrl":"https://doi.org/10.1186/s12874-024-02363-7","url":null,"abstract":"<p><strong>Background: </strong>In group-sequential designs, it is typically assumed that there is no time gap between patient enrollment and outcome measurement in clinical trials. However, in practice, there is usually a lag between the two time points. This can affect the statistical analysis of the data, especially in trials with interim analyses. One approach to address delayed responses has been introduced by Hampson and Jennison (J R Stat Soc Ser B Stat Methodol 75:3-54, 2013), who proposed the use of error-spending stopping boundaries for patient enrollment, followed by critical values to reject the null hypothesis if the stopping boundaries are crossed beforehand. Regarding the choice of a trial design, it is important to consider the efficiency of trial designs, e.g. in terms of the probability of trial success (power) and required resources (sample size and time).</p><p><strong>Methods: </strong>This article aims to shed more light on the performance comparison of group sequential clinical trial designs that account for delayed responses and designs that do not. Suitable performance measures are described and designs are evaluated using the R package rpact. By doing so, we provide insight into global performance measures, discuss the applicability of conditional performance characteristics, and finally whether performance gain justifies the use of complex trial designs that incorporate delayed responses.</p><p><strong>Results: </strong>We investigated how the delayed response group sequential test (DR-GSD) design proposed by Hampson and Jennison (J R Stat Soc Ser B Stat Methodol 75:3-54, 2013) can be extended to include nonbinding lower recruitment stopping boundaries, illustrating that their original design framework can accommodate both binding and nonbinding rules when additional constraints are imposed. Our findings indicate that the performance enhancements from methods incorporating delayed responses heavily rely on the sample size at interim and the volume of data in the pipeline, with overall performance gains being limited.</p><p><strong>Conclusion: </strong>This research extends existing literature on group-sequential designs by offering insights into differences in performance. We conclude that, given the overall marginal differences, discussions regarding appropriate trial designs can pivot towards practical considerations of operational feasibility.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"242"},"PeriodicalIF":3.9,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457875","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":"Co-creation of a step-by-step guide for specifying the test-management pathway to formulate focused guideline questions about healthcare related tests.","authors":"Mariska K Tuut, Gowri Gopalakrishna, Mariska M Leeflang, Patrick M Bossuyt, Trudy van der Weijden, Jako S Burgers, Miranda W Langendam","doi":"10.1186/s12874-024-02365-5","DOIUrl":"https://doi.org/10.1186/s12874-024-02365-5","url":null,"abstract":"<p><strong>Background: </strong>Guideline development on testing is known to be difficult for guideline developers. It requires consideration of various aspects, such as accuracy, purpose of testing, and consequences on management and people-important outcomes. This can be outlined in a test-management pathway. We aimed to create and user-test a step-by-step guide for guideline developers for designing a test-management pathway.</p><p><strong>Methods: </strong>Developmental design with a co-creative strategy. We created a draft step-by-step guide, that was user tested in a workshop with 19 experts, and by interviewing 7 guideline panel members.</p><p><strong>Results: </strong>Our proposed guide consists of five blocks of signalling questions: patients/population, index test(s), current practice/comparison/control, people-important outcomes, and the link between testing and outcome(s). The user testing led to refinement of the signalling questions, the use of inclusive terminology, and addition of a test-management pathway figure with detailed explanation.</p><p><strong>Conclusions: </strong>The step-by-step guide for formulating focused guideline questions regarding healthcare related testing can help in identifying relevant characteristics of the population, tests, and outcomes and to create a test management pathway. This should facilitate the formulation of evidence-based guideline recommendations about healthcare related testing.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"241"},"PeriodicalIF":3.9,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11481243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457862","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}