Clinical Trials最新文献

{"title":"A modular pipeline for natural language processing-screened human abstraction of a pragmatic trial outcome from electronic health records.","authors":"Robert Y Lee, Kevin S Li, James Sibley, Trevor Cohen, William B Lober, Janaki O'Brien, Nicole LeDuc, Kasey Mallon Andrews, Anna Ungar, Jessica Walsh, Elizabeth L Nielsen, Danae G Dotolo, Erin K Kross","doi":"10.1177/17407745251405386","DOIUrl":"10.1177/17407745251405386","url":null,"abstract":"<p><strong>Background: </strong>Natural language processing allows efficient extraction of clinical variables and outcomes from electronic health records (EHRs). However, measuring pragmatic clinical trial outcomes may demand accuracy that exceeds natural language processing performance. Combining natural language processing with human adjudication can address this gap, yet few software solutions support such workflows. We developed a modular, scalable system for natural language processing-screened human abstraction to measure the primary outcomes of two clinical trials.</p><p><strong>Methods: </strong>In two clinical trials of hospitalized patients with serious illness, a deep-learning natural language processing model screened electronic health record passages for documented goals-of-care discussions. Screen-positive passages were referred for human adjudication using a REDCap-based system to measure the trial outcomes. Dynamic pooling of passages using structured query language within the REDCap database reduced unnecessary abstraction while ensuring data completeness.</p><p><strong>Results: </strong>In the first trial (N = 2512), natural language processing identified 22,187 screen-positive passages (0.8%) from 2.6 million electronic health record passages. Human reviewers adjudicated 7494 passages over 34.3 abstractor-hours to measure the cumulative incidence and time to first documented goals-of-care discussion for all patients with 92.6% patient-level sensitivity. In the second trial (N = 617), natural language processing identified 8952 screen-positive passages (1.6%) from 559,596 passages at a threshold with near-100% sensitivity. Human reviewers adjudicated 3509 passages over 27.9 abstractor-hours to measure the same outcome for all patients.</p><p><strong>Discussion: </strong>We present the design and source code for a scalable and efficient pipeline for measuring complex electronic health record-derived outcomes using natural language processing-screened human abstraction. This implementation is adaptable to diverse research needs, and its modular pipeline represents a practical middle ground between custom software and commercial platforms.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"145-154"},"PeriodicalIF":2.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12912770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146178017","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":"Practical inference for a complier average causal effect in cluster randomised trials with a binary outcome.","authors":"Tansy Edwards, Jennifer Thompson, Charles Opondo, Elizabeth Allen","doi":"10.1177/17407745251378407","DOIUrl":"10.1177/17407745251378407","url":null,"abstract":"<p><strong>Background: </strong>Individual non-compliance with an intervention in cluster randomised trials can occur and estimating an intervention effect according to intention-to-treat ignores non-compliance and underestimates efficacy. The effect of the intervention among compliers (the complier average causal effect) provides an unbiased estimate of efficacy but inference can be complex in cluster randomised trials.</p><p><strong>Methods: </strong>We evaluated the performance of a pragmatic bootstrapping approach accounting for clustering to obtain a 95% confidence interval (CI) for a CACE for cluster randomised trials with monotonicity and one-sided non-compliance. We investigated a variety of scenarios for correlated cluster-level prevalence of a binary outcome and non-compliance (5%, 10%, 20%, 30%, 40%). Cluster randomised trials were simulated with the minimum number of clusters to provide at least 80% and at least 90% power, to detect an ITT odds ratio (OR) of 0.5 with 100 individuals per cluster.</p><p><strong>Results: </strong>Under all non-compliance scenarios (5%-40%), there was negligible bias for the CACE. In the worst-case of bias, a true OR of 0.18 was estimated as 0.15 for the rarest outcome (5%) and highest non-compliance (40%). There was no under-coverage of bootstrap CIs. CIs were the correct width for an outcome prevalence of 20%-40% but too wide for a less common outcome. Loss of power for a CACE bootstrap analysis versus ITT regression analysis increased as the prevalence of the outcome decreased across all non-compliance scenarios, particularly for an outcome prevalence of less than 20%.</p><p><strong>Conclusions: </strong>Our bootstrapping approach provides an accessible and computationally simple method to evaluate efficacy in support of ITT analyses in cluster randomised trials.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"33-42"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12909608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145298963","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":"Charting the content of data monitoring committee charters for clinical trials.","authors":"Lisa Eckstein, Akram Ibrahim, Olivia Orr, Annette Rid, Seema K Shah","doi":"10.1177/17407745251389185","DOIUrl":"10.1177/17407745251389185","url":null,"abstract":"<p><strong>Background: </strong>Data monitoring committees play a critical role in ensuring the ethical conduct of clinical trials. Data monitoring committee charters set out the role and processes for data monitoring committees in monitoring clinical trials; however, little is known about the information charters contain.</p><p><strong>Methods: </strong>We conducted a summative content analysis of a convenience sample of data monitoring committee charters based on the criteria set out for charters by the DAMOCLES Study Group in 2005. Thirteen charters from public and commercially sponsored clinical trials were obtained for review.</p><p><strong>Results: </strong>Although the data monitoring committee charters we analyzed broadly satisfied the criteria set out by the DAMOCLES Study Group, some issues warrant further attention. These included variability in the availability of unmasked data for review, communication across data monitoring committees for related trials, post-trial DMC responsibilities, and a need for more explicit decision-making processes and conflict resolution procedures. Moreover, few of the data monitoring committee charters we were able to analyze included legal protection for members.</p><p><strong>Conclusion: </strong>Despite limitations due to the difficulties in obtaining data monitoring committee charters, the convenience sample reviewed suggests variability, including in terms of implementation of some best-practice recommendations. There is a need for further exploration of these issues in a larger sample size. Undertaking such research would be assisted by requiring or incentivizing public access to data monitoring committee charters.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"121-126"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12841360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145713342","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":"Factors associated with enrollment in a randomized clinical trial of docosahexaenoic supplementation in toddlers born preterm.","authors":"Kelly M Boone, Amanda Miglin, Paige L Taylor, Mark A Klebanoff, Sarah A Keim","doi":"10.1177/17407745251387983","DOIUrl":"10.1177/17407745251387983","url":null,"abstract":"<p><p>Background/aimsTo assess pre- and postnatal factors associated with participation in a randomized clinical trial of daily docosahexaenoic supplementation in toddlers born preterm. We hypothesized that enrolled families would not differ from those who did not participate.MethodChildren eligible for the Omega Tots trial were born at <35 completed weeks' gestation and were 10-16 months of age at recruitment. Eligibility data abstracted from the medical record were linked with the child's birth certificate. The primary outcome was whether the family enrolled, declined, or was non-responsive to recruitment efforts. Log-binomial regression calculated risk ratios (RR).Results316 families enrolled, 1089 declined, and 1081 were non-responsive. Enrolling, rather than not enrolling, was negatively associated with caregivers being married (RR = 0.76, 95% CI: 0.62, 0.94), identifying as White (RR = 0.76, 95% CI: 0.60, 0.94), and children being born at later gestational ages (RR1-week = 0.96, 95% CI: 0.92, 0.99); positively associated with children weighing <1500 g at birth (RR = 1.26, 95% CI: 1.01, 1.55), attending a neonatology specialty clinic (RR = 1.46, 95% CI: 1.19, 1.80), family participation in WIC (RR = 1.39, 95% CI: 1.13, 1.72), and living in an urban zip code (RR = 1.68, 95% CI: 1.30, 2.17). Varied associations with enrolling rather than declining, enrolling rather than being non-responsive, and declining rather than being non-responsive were identified.ConclusionsMaternal, child, and socioeconomic characteristics were different for families who enrolled, relative to families who did not enroll. Factors associated with enrollment differed between families who were non-responsive to recruitment attempts and those who declined enrollment, with additional differences identified between families who declined participation and those who were non-responsive. Recruitment initiatives tailored to ensuring enrollees reflect the source population may improve generalizability.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"99-108"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12755725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145854798","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":"How is missing data handled in cluster randomized controlled trials? A review of trials published in the NIHR Journals Library 1997-2024.","authors":"Siqi Wu, Richard M Jacques, Stephen J Walters","doi":"10.1177/17407745251378117","DOIUrl":"10.1177/17407745251378117","url":null,"abstract":"<p><strong>Background: </strong>Cluster randomized controlled trials are increasingly used to evaluate the effectiveness of interventions in clinical and public health research. However, missing data in cluster randomized controlled trials can lead to biased results and reduce statistical power if not handled appropriately. This study aimed to review, describe and summarize how missing primary outcome data are handled in reports of publicly funded cluster randomized controlled trials.</p><p><strong>Methods: </strong>This study reviewed the handling of missing data in cluster randomized controlled trials published in the UK National Institute for Health and Care Research Journals Library from 1 January 1997 to 31 December 2024. Data extraction focused on trial design, missing data mechanisms, handling methods in primary analyses and sensitivity analyses.</p><p><strong>Results: </strong>Among the 110 identified cluster randomized controlled trials, 45% (50/110) did not report or take any action on missing data in either primary analysis or sensitivity analysis. In total, 75% (82/110) of the identified cluster randomized controlled trials did not impute missing values in their primary analysis. Advanced methods like multiple imputation were applied in only 15% (16/110) of primary analyses and 28% (31/110) of sensitivity analyses. On the contrary, the review highlighted that missing data handling methods have evolved over time, with an increasing adoption of multiple imputation since 2017. Overall, the reporting of how missing data is handled in cluster randomized controlled trials has improved in recent years, but there are still a large proportion of cluster randomized controlled trials lack of transparency in reporting missing data, where essential information such as the assumed missing mechanism could not be extracted from the reports.</p><p><strong>Conclusion: </strong>Despite progress in adopting multiple imputation, inconsistent reporting and reliance on simplistic methods (e.g. complete case analysis) undermine cluster randomized controlled trial credibility. Recommendations include stricter adherence to CONSORT guidelines, routine sensitivity analyses for different missing mechanisms and enhanced training in advanced imputation techniques. This review provides updated insights into how missing data are handled in cluster randomized controlled trials and highlight the urgency for methodological transparency to ensure robust evidence generation in clustered trial designs.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"75-84"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12909601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225050","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":"Confirmatory evidence supporting single pivotal trial new drug approvals by the Food and Drug Administration, 2015 through 2023.","authors":"Carla Barile Godoy, Reshma Ramachandran, Pradyumna Sapre, Joseph S Ross","doi":"10.1177/17407745251376620","DOIUrl":"10.1177/17407745251376620","url":null,"abstract":"<p><strong>Backgrounds/aims: </strong>To secure market authorization, the Food and Drug Administration requires that drug manufacturers demonstrate product safety and efficacy for an indicated use based on two adequate and well-controlled studies, known as pivotal clinical trials. A single pivotal trial may also be sufficient for product approval, however, if safety and efficacy is clearly and convincingly demonstrated, or if accompanied by confirmatory evidence. We examined all original drug and biologic indication approvals by the Food and Drug Administration between 2015 and 2023 to determine what proportion of those approved on the basis of a single pivotal trial were accompanied by confirmatory evidence, the type and strength of this evidence, and whether confirmatory evidence was cited more frequently after December 2019, when the Food and Drug Administration released draft guidance clarifying issues related to confirmatory evidence.</p><p><strong>Methods: </strong>Information was extracted from publicly available Food and Drug Administration documents, and we used descriptive statistics to characterize the sample and chi-square tests to compare the frequency with which confirmatory evidence was cited before and after December 2019.</p><p><strong>Results: </strong>Overall, the Food and Drug Administration approved 441 original drug and biologic indications between 2015 and 2023; 40 of which were excluded. Of the remaining, 181 (41%) were based on 2 or more pivotal trials, 35 (7.9%) on a single pivotal trial with at least one clinical primary efficacy endpoint without orphan designation, and 185 (42%) on a single pivotal trial. Among the final category of approvals, the Food and Drug Administration explicitly referenced confirmatory evidence for 36 (19.5%) single pivotal trial approvals and implicitly referenced confirmatory evidence for 4 (2.2%) others. These 40 approvals referenced 99 unique sources of confirmatory evidence, most commonly pharmacodynamic/mechanistic (n = 49) and other (n = 32). Reference to confirmatory evidence was greater after the Food and Drug Administration issued clarifying guidance in December 2019 (pre: 7% vs post: 34%; p < 0.0001).</p><p><strong>Conclusions: </strong>Given the rising number of the Food and Drug Administration approvals based on a single pivotal trial, greater clarity on confirmatory evidence standards and communication of its use could be considered.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"109-112"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meta-analytic evaluation of surrogate endpoints at multiple time points in randomized controlled trials with time-to-event endpoints.","authors":"Xiaoyu Tang, Ludovic Trinquart","doi":"10.1177/17407745251377734","DOIUrl":"10.1177/17407745251377734","url":null,"abstract":"<p><strong>Background: </strong>Valid surrogate endpoints are of great interest for efficient evaluation of novel therapies. With surrogate and true time-to-event endpoints, meta-analytic approaches for surrogacy validation commonly rely on the hazard ratio, ignore that randomized trials possibly contribute to the meta-analysis for different follow-up durations, overlook the importance of the time lag between surrogate and true endpoints in determining surrogate utility, and assume that treatment effects and the strength of surrogacy remain constant over time. In this context, we introduce a novel two-stage meta-analytic model to evaluate trial-level surrogacy.</p><p><strong>Methods: </strong>Our model employs restricted mean survival time (RMST) differences to quantify treatment effects at the first stage. At the second stage, the model is based on the between-study covariance matrix of RMSTs and differences in RMST to assess surrogacy through coefficients of determination at multiple timepoints. This framework integrates estimates from each component RCT without extrapolation beyond the trial-specific time support, can explicitly model a time lag between endpoints, and remains valid under non-proportional hazards.</p><p><strong>Results: </strong>Simulation studies indicate that our model yields unbiased and precise estimates of the coefficient of determination. In an individual patient data meta-analysis in gastric cancer, estimates of coefficients of determination from our model reflect the temporal lag between endpoints and reveal dynamic changes in surrogacy strength over time compared to the Clayton survival copula model, a widely used reference method in surrogate endpoint validation for time-to-event outcomes.</p><p><strong>Conclusion: </strong>Our new meta-analytic model to evaluate trial-level surrogacy using the difference in RMST as the measure of treatment effect does not require the proportional hazard assumption, captures the strength of surrogacy at multiple time points, and can evaluate surrogacy with a time lag between surrogate and true endpoints. The proposed method enhances the rigor and practicality of surrogate endpoint validation in time-to-event settings.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"13-22"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12614294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145298965","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":"Effects of health technology use and digital health engagement on clinical trial Participation: Findings from the Health Information National Trends Survey.","authors":"Nicholas C Peiper, Stephen Furmanek, Kelly C McCants, Edward H Brown","doi":"10.1177/17407745251387981","DOIUrl":"10.1177/17407745251387981","url":null,"abstract":"<p><p>Background/AimsThe existing literature indicates that clinical trial knowledge and participation is multifactorial, yet little is known about the association with digital health technology use and digital health engagement. To address this gap, we examined the multivariate association between clinical trial knowledge and participation with past-year health technology use and digital health engagement with medical providers using data from a federal surveillance system in the United States.MethodsA total of 3865 US adult respondents from the Health Information National Trends Survey 5, Cycle 4 provided data in 2020. The two outcomes were clinical trial knowledge (no knowledge, a little knowledge, a lot of knowledge) and participation (never invited, invited did not participate, invited and participated). There were four binary indicators of health technology use for the following purposes in the past year: searching for health or medical information, communicating with a doctor's office, looking up medical test results, and making medical appointments. There were four binary indicators of digital health engagement in the past year: sharing health information on social media, participating in a health forum or support group, watching health-related videos on YouTube, and awareness of ClinicalTrials.gov.ResultsSurvey-weighted multivariate regression models demonstrated that awareness of ClinicalTrials.gov had the greatest associations with clinical trial knowledge (adjusted risk ratio = 7.60, 95% confidence interval: 4.82-12.00) and participation (adjusted risk ratio = 2.60, 95% confidence interval: 1.23-5.54). Using digital technology to look for health information (adjusted risk ratio = 1.35, 95% confidence interval: 1.06-1.71) and communicate with doctor's offices were significantly associated with higher clinical trial knowledge (adjusted risk ratio = 1.64, 95% confidence interval: 1.25-2.14). Involvement in an online forum or support group was significantly associated with an increased likelihood of being invited but not participating in a clinical trial (adjusted risk ratio = 2.32, 95% confidence interval: 1.22-4.39), while using digital technology to make medical appointments was significantly associated with an increased likelihood of clinical trial participation (adjusted risk ratio = 1.79, 95% confidence interval: 1.07-2.99).ConclusionsFindings from this study can inform the design of large-scale digital health campaigns and quality improvement programs focused on increasing clinical trial participation.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"43-53"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145667570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Desirability of outcome ranking (DOOR) analysis for multivariate survival outcomes with application to ACTT-1 trial.","authors":"Shiyu Shu, Guoqing Diao, Toshimitsu Hamasaki, Scott Evans","doi":"10.1177/17407745251385582","DOIUrl":"10.1177/17407745251385582","url":null,"abstract":"<p><strong>Background: </strong>Desirability of outcome ranking (DOOR) is a paradigm for the design, monitoring, analysis, interpretation, and reporting of clinical trials based on patient-centric benefit-risk evaluation, developed to address limitations of existing approaches and advance clinical trial science. The first step in implementing DOOR is defining an ordinal DOOR outcome representing a global patient-centric response, a cumulative summary of the benefits and harms for an individual patient. This article aims to develop an analysis methodology for the setting where the DOOR outcome is a progressive time-varying state, and there is interest in event times and times that patients spend in more and less desirable states.</p><p><strong>Methods: </strong>We develop methods to estimate and make inferences about the temporal treatment effects. If the k-levels of the DOOR outcome are monotone, then k - 1 non-overlapping Kaplan-Meier survival curves can be estimated and plotted. The areas under the curves asymptotically follow a multivariate Gaussian distribution. We apply restricted mean survival time (RMST) concepts to the ordinal Kaplan-Meier curves and provide steps for estimating the covariance structure.</p><p><strong>Results: </strong>Simulation studies demonstrate that the proposed methods perform well in practical settings. We generate censoring time under a uniform distribution and event times under a multi-state structure. The proposed estimators have small biases, the 95% confidence intervals have correct coverage probabilities, and the proposed tests accurately control the type I error rate under the null hypothesis. We illustrate the methods using data from Adaptive COVID-19 Treatment Trial (ACTT-1), a clinical trial that compared remdesivir vs placebo for the treatment of COVID-19 infection.</p><p><strong>Discussion: </strong>Ordinal DOOR outcomes, which incorporate benefits and harms and represent an overall patient response, have recently been recommended by the Council for International Organizations of Medical Sciences (CIOMS) as a standard approach to benefit:risk analysis. Such endpoints recognize the cumulative nature of outcomes on patients, account for correlations between efficacy and safety, incorporate multivariate survival outcomes, offer generalizability to inform clinical practice, and recognize finer gradations of patient response and binary outcomes. Robust and interpretable analysis methodologies for ordinal outcomes are needed.</p><p><strong>Conclusion: </strong>Restricted mean survival time is a useful nonparametric approach for robust treatment effect estimation. We provide a framework for inference using multiple RMSTs to analyze DOOR and other ordinal outcomes using an interpretable time metric.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"23-32"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145833111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of use of external data and update on reporting standards in Sequential Multiple-Assignment Randomised Trials.","authors":"Isaac J Egesa, Laura Bonnett, Richard Emsley, Anthony Marson, Catrin T Smith","doi":"10.1177/17407745251385535","DOIUrl":"10.1177/17407745251385535","url":null,"abstract":"<p><strong>Background: </strong>The Sequential Multiple-Assignment Randomised Trial (SMART) design is considered the gold standard for developing adaptive interventions, which tailor treatments to individual patient characteristics and responses. While SMART offers a rigorous framework aligned with real-world clinical decision-making, it is often complex, time-consuming, and costly. As interest in SMART design grows, there is increasing recognition for the need to improve its implementation through more explicit guidance and best practices. Efficiency gains may also be possible by incorporating external data to inform their design, conduct, and analysis. This review aimed to identify all published trials using the SMART design, summarise their design, conduct, and reporting practices and evaluate the use of external data in their implementation.</p><p><strong>Methods: </strong>We searched PubMed, Medline, PsycINFO, Scopus, and Web of Science databases for all SMART up to June 30, 2024. External data were defined as non-simulated individual patient data collected outside the main SMART to supplement or inform the main trial.</p><p><strong>Results: </strong>We included 80 SMART, of which 35 (44%) were completed and 45 (56%) were ongoing. Most trials reported two phases of randomisation (93%), with the primary aim focusing on evaluating main effects (81%) of interventions at the first stage of randomisation. There was inadequate reporting of several key aspects, including sample size estimation, statistical analysis software, allocation concealment, data missingness, multiple testing, sensitivity analysis, and the use of SMART in the title. Seventeen (21%) SMART (4-completed trials and 13-trial protocols) referred to the use of external data from electronic health records (n = 12) and registries (n = 5). External data was used for recruitment (n = 11), outcome measures (n = 6), and to provide baseline covariate information (n = 1).</p><p><strong>Conclusion: </strong>SMART designs are increasingly used to develop adaptive interventions across diverse clinical contexts, yet key methodological features and basic components remain inconsistently reported. This limits transparency, reproducibility, and potential for translation into routine care. Although external data are widely used in standard randomised controlled trials, their use in the SMART is still limited, likely due to methodological and infrastructural challenges and the absence of tailored reporting standards. To improve the efficiency and generalisability of SMART designs, expert-led extensions of CONSORT and SPIRIT guidelines are needed, including specific recommendations for reporting external data use. Future research should explore optimal external data sources for informing SMART components and promote interdisciplinary collaboration and training to support high-quality implementation.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"85-98"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12909610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145444073","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}