Clinical Trials最新文献

{"title":"Characterization of studies considered and required under Medicare's coverage with evidence development program.","authors":"Maryam Mooghali, Osman Moneer, Guneet Janda, Joseph S Ross, Sanket S Dhruva, Reshma Ramachandran","doi":"10.1177/17407745251313979","DOIUrl":"10.1177/17407745251313979","url":null,"abstract":"<p><p>IntroductionIn 2005, the Centers for Medicare and Medicaid Services introduced the Coverage with Evidence Development program for items and services with limited evidence of benefit and harm for Medicare beneficiaries, aiming to generate evidence to determine whether they meet the statutory \"reasonable and necessary\" criteria for coverage. Coverage with Evidence Development requires participation in clinical studies approved by the Centers for Medicare and Medicaid Services (i.e. Coverage with Evidence Development-approved studies) as a condition of coverage. We examined the quality of evidence generated by Coverage with Evidence Development-approved studies compared with those that informed Centers for Medicare and Medicaid Services' initial Coverage with Evidence Development decisions (i.e. National Coverage Determination studies).MethodsUsing Centers for Medicare and Medicaid Services' webpage, we identified all items and services covered under Coverage with Evidence Development and their Coverage with Evidence Development-approved studies. Through searching PubMed and Google Scholar, we identified original research articles that reported results for primary endpoints of Coverage with Evidence Development-approved studies. We then reviewed the initial Coverage with Evidence Development decision memos and identified National Coverage Determination studies that were original research.We characterized and compared Coverage with Evidence Development-approved studies and National Coverage Determination studies.ResultsFrom 2005 to 2023, 26 items and services were covered under the Coverage with Evidence Development program, associated with 196 National Coverage Determination studies (170 (86.7%) clinical trials and 26 (13.3%) registries) and 116 unique Coverage with Evidence Development-approved studies (86 (74.1%) clinical trials, 23 (19.8%) registries, 4 (3.4%) claims-based studies, and 3 (2.6%) expanded access studies). Among clinical trial studies, National Coverage Determination studies and Coverage with Evidence Development-approved studies did not differ with respect to multi-arm design (59.4% vs 68.6%; <i>p</i> = 0.15). However, among multi-arm clinical trial studies, National Coverage Determination studies were less likely than Coverage with Evidence Development-approved studies to be randomized (52.5% vs 93.2%; <i>p</i> < 0.001). Overall, National Coverage Determination studies less frequently had ≥ 1 primary endpoint focused on a clinical outcome measure (65.8% vs 87.9%; <i>p</i> = 0.006) and less frequently exclusively enrolled Medicare beneficiaries (3.1% vs 25.9%; <i>p</i> < 0.001). In addition, National Coverage Determination studies had smaller population sizes than Coverage with Evidence Development-approved studies (median 100 (interquartile range, 45-414) vs 302 (interquartile range, 93-1000) patients; <i>p</i> = 0.002). Among Coverage with Evidence Development-approved studies, 59 (50.9%) had not yet publicly reported resul","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"619-625"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143374119","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":"Precision medicine evaluation of heterogeneity of treatment effect for a time-to-event outcome with application in a trial of Initial treatment for people living with HIV.","authors":"Yu Zheng, Judy S Currier, Michael D Hughes","doi":"10.1177/17407745251338558","DOIUrl":"10.1177/17407745251338558","url":null,"abstract":"<p><p>BackgroundEvaluation of heterogeneity of treatment effect among participants in large randomized clinical trials may provide insights as to the value of individualizing clinical decisions. The effect modeling approach to predictive heterogeneity of treatment effect analysis offers a promising framework for heterogeneity of treatment effect estimation by simultaneously considering multiple patient characteristics and their interactions with treatment to predict differences in outcomes between randomized treatments. However, its implementation in clinical research remains limited and so we provide a detailed example of its application in a randomized trial that compared raltegravir-based vs darunavir/ritonavir-based therapy as initial antiretroviral treatments for people living with HIV.MethodsThe heterogeneity of treatment effect analysis used a two-step procedure, in which a working proportional hazards model was first selected to construct an index score for ranking the treatment difference for individuals, and then a second calibration step used a non-parametric kernel approach to estimate the true treatment difference for participants with similar index scores. Sensitivity and supplemental analyses were conducted to evaluate the robustness of the results. We further explored the impact of covariates on heterogeneity of treatment effect and the choice between treatments.ResultsThe heterogeneity of treatment effect analysis showed that while there is a clear trend favoring raltegravir-based therapy over darunavir/ritonavir-based therapy for the vast majority of the target population, there were a small subset of patients, characterized by more advanced HIV disease status, for whom the choice between the two treatments might be equivocal.ConclusionsThrough this example, we illustrate how an exploratory heterogeneity of treatment effect analysis might provide further insights into the comparative efficacy of treatments evaluated in a randomized trial. We also highlight some of the issues in implementing and interpreting effect modeling analyses in randomized trials.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"559-570"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12353116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119117","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":"Standardising management of consent withdrawal and other clinical trial participation changes: The UKCRC Registered Clinical Trials Unit Network's PeRSEVERE project.","authors":"William J Cragg, Laura Clifton-Hadley, Jeremy Dearling, Susan J Dutton, Katie Gillies, Pollyanna Hardy, Daniel Hind, Søren Holm, Kerenza Hood, Anna Kearney, Rebecca Lewis, Sarah Markham, Lauren Moreau, Tra My Pham, Amanda Roberts, Sharon Ruddock, Mirjana Sirovica, Ratna Sohanpal, Puvan Tharmanathan, Rejina Verghis","doi":"10.1177/17407745251344524","DOIUrl":"10.1177/17407745251344524","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;Existing regulatory and ethical guidance does not address real-life complexities in how clinical trial participants' level of participation may change. If these complexities are inappropriately managed, there may be negative consequences for trial participants and the integrity of trials they participate in. These concerns have been highlighted over many years, but there remains no single, comprehensive guidance for managing participation changes in ways that address real-life complexities while maximally promoting participant interests and trial integrity. Motivated by the lack of agreed standards, and observed variability in practice, representatives from academic clinical trials units and linked organisations in the United Kingdom initiated the PeRSEVERE project (PRincipleS for handling end-of-participation EVEnts in clinical trials REsearch) to agree on guiding principles and explore how these principles should be implemented.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed the PeRSEVERE principles through discussion and debate within a large, multidisciplinary collaboration, including research professionals and public contributors. We took an inclusive approach to drafting the principles, incorporating new ideas if they were within project scope. Our draft principles were scrutinised through an international consultation survey which focussed on the principles' clarity, feasibility, novelty and acceptability. Survey responses were analysed descriptively (for category questions) and using a combination of deductive and inductive analysis (for open questions). We used predefined rules to guide feedback handling. After finalising the principles, we developed accompanying implementation guidance from several sources.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 280 people from 9 countries took part in the consultation survey. Feedback showed strong support for the principles with 96% of respondents agreeing with the principles' key messages. Based on our predefined rules, it was not necessary to amend our draft principles, but comments were nonetheless used to enhance the final project outputs. Our 17 finalised principles comprise 7 fundamental, 'overarching' principles, 6 about trial design and setup, 2 covering data collection and monitoring, and 2 on trial analysis and reporting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;We devised a comprehensive set of guiding principles, with detailed practical recommendations, to aid the management of clinical trial participation changes, building on existing ethical and regulatory texts. Our outputs reflect the contributions of a substantial number of individuals, including public contributors and research professionals with various specialisms. This lends weight to our recommendations, which have implications for everyone who designs, funds, conducts, oversees or participates in trials. We suggest our principles could lead to improved standards in clinical trials and better exper","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"578-596"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144559362","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":"Quality management of a multi-center randomized controlled feeding trial: A prospective observational study.","authors":"Xiayan Chen, Huijuan Li, Lin Feng, Xi Lan, Shuyi Li, Yanfang Zhao, Guo Zeng, Huilian Zhu, Jianqin Sun, Yanfang Wang, Yangfeng Wu","doi":"10.1177/17407745251324653","DOIUrl":"10.1177/17407745251324653","url":null,"abstract":"<p><p>BackgroundNutrition and dietary trials are often prone to bias, leading to inaccurate or questionable estimates of intervention efficacy. However, reports on quality management practices of well-controlled dietary trials are scarce. This study aims to introduce the quality management system of the Diet, ExerCIse and CarDiovascular hEalth-Diet Study and report its performance in ensuring study quality.MethodsThe quality management system consisted of a study coordinating center, trial governance, and quality control measures covering study design, conduct, and data analysis and reporting. Metrics for evaluating the performance of the system were collected throughout the whole trial development and conducted from September 2016 to June 2021, covering major activities at the coordinating center, study sites, and central laboratories, with a focus on the protocol amendments, protocol deviations (eligibility, fidelity, confounders management, loss to follow-up and outside-of-window visits, and blindness success), and measurement accuracy.ResultsThree amendments to the study protocol enhanced feasibility. All participants (265) met the eligibility criteria. Among them, only 3% were lost to the primary outcome follow-up measurement. More than 95% of participants completed the study, they consumed more than 96% of the study meals, and more than 94% of participants consumed more than 18 meals per week, with no between-group differences. Online monitoring of nutrient targets for the intervention diet showed that all targets were achieved except for the fiber intake, which was 4.3 g less on average. Only 3% experienced a body weight change greater than 2.0 kg, and 3% had medication changes which were not allowed by the study. James' blinding index at the end of the study was 0.68. The end digits of both systolic and diastolic blood pressure readings were distributed equally. For laboratory measures, 100% of standard samples, 97% of blood-split samples, and 87% of urine-split samples had test results within the acceptable range. Only 1.4% of data items required queries, for which only 30% needed corrections.DiscussionThe Diet, ExerCIse and CarDiovascular hEalth-Diet Study quality management system provides a framework for conducting a high-quality dietary intervention clinical trial.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"527-537"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751500","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":"Evaluating the use of text-message reminders and personalised text-message reminders on the return of participant questionnaires in trials, a systematic review and meta-analysis.","authors":"Laura Doherty, Catherine Arundel, Elizabeth Coleman, Ailish Byrne, Katherine Jones","doi":"10.1177/17407745251320888","DOIUrl":"10.1177/17407745251320888","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Randomised controlled trials are widely accepted as the gold standard research methodology for the evaluation of interventions. However, they often display poor participant retention. To prevent this, various participant interventions have been identified and evaluated through the use of studies within a trial. Two such interventions are participant short message service reminders (also known as text-messages) and personalised participant short message service reminders, designed to encourage a participant to return a study questionnaire. While previous studies within a trial have evaluated the effectiveness of these two retention strategies, trialists continue to spend both time and money on these strategies while the evidence remains inconclusive.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This systematic review and meta-analysis compared the use of short message service reminders with no short message service reminder and personalised short message service reminders with non-personalised short message service reminders, on participant retention. Eligible studies were identified through advanced searches of electronic databases (MEDLINE, EMBASE and Cochrane Library) and hand-searching of alternative information sources. The review primary outcome was the proportion of study questionnaires returned for the individual study within a trial primary analysis time points.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Nine eligible studies within a trial were identified, of which four compared short message service versus no short message service and five compared personalised short message service versus non-personalised short message service. For those that compared personalised short message service versus non-personalised short message service, only three were deemed appropriate for meta-analysis. The primary outcome results for short message service versus no short message service concluded that short message service led to a statistically non-significant increase in the odds of study questionnaire return by 9% (odds ratio = 1.09, 95% confidence interval = 0.92 to 1.30). Similarly, comparison of personalised short message service versus non-personalised short message service concluded that personalised short message service caused a statistically non-significant increase in odds by 22% (odds ratio = 1.22, 95% confidence interval = 0.95 to 1.59).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The effectiveness of both short message service and personalised short message service as retention tools remains inconclusive and further study within a trial evaluations are required. However, as short message services are low in cost, easy to use and generally well accepted by participants, it is suggested that trialists adopt a pragmatic approach and utilise these reminders until further research is conducted. Given both the minimal addition in cost for studies already utilising short message service reminders and some evidence of effect, personalisation shoul","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"607-618"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604142","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":"Developing a research coordinator workforce: A case study of a hospital and university collaboration.","authors":"April M Crawford, Steven L Arxer, James P LePage","doi":"10.1177/17407745251338574","DOIUrl":"10.1177/17407745251338574","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"632-634"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144257494","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":"Identification and mitigation of a systematic analysis error in a multicenter dual-energy x-ray absorptiometry study.","authors":"Gayle M Lorenzi, Barbara H Braffett, Ionut Bebu, Victoria R Trapani, Jye-Yu C Backlund, Kaleigh Farrell, Rose A Gubitosi-Klug, Ann V Schwartz","doi":"10.1177/17407745251328257","DOIUrl":"10.1177/17407745251328257","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Background/AimsData integrity in multicenter and longitudinal studies requires implementation of standardized reproducible methods throughout the data collection, analysis, and reporting process. This requirement is heightened when results are shared with participants that may influence health care decisions. A quality assurance plan provides a framework for ongoing monitoring and mitigation strategies when errors occur.MethodsThe Diabetes Control and Complications Trial (1983-1993) and its follow-up study, the Epidemiology of Diabetes Interventions and Complications (1994-present), have characterized risk factors and long-term complications in a type 1 diabetes cohort followed for over 40 years. An ancillary study to assess bone mineral density was implemented across 27 sites, using one of two dual x-ray absorptiometry scanner types. Centrally generated reports were distributed to participants by the sites. A query from a site about results that were incongruent with a single participant's clinical history prompted reevaluation of this scan, revealing a systematic error in the reading of hip scans from one of the two scanner types. A mitigation plan was implemented to correct and communicate the errors to ensure participant safety, particularly among those originally identified as having low bone mineral density scores for whom antiresorptive treatment may have been initiated based on these results.ResultsThe error in the analysis of hip scans from the identified scanner type resulted in lower bone mineral density scores in scans requiring manual deletion of the ischium bone. Hip scans with original T-score ≤ -2.5 (n = 84) acquired on either scanner were reviewed, and reanalyzed if the error was detected. Fourteen scans were susceptible to this error and reanalyzed: nine scans were reclassified from osteoporosis to low bone mineral density, one from low to normal bone mineral density, and four were unchanged. All errors occurred on one scanner type. An integrated communication and intervention plan was implemented. The nine participants whose scans were reclassified from osteoporosis to low bone mineral density were contacted; five were using antiresorptive treatment, all of whom had other risk factors for fracture beyond these scan results. Review of all hip scans with a T-score &gt; -2.5 (n = 371) using this scanner type identified 27 additional hip scans that required reanalysis and potential reclassification: 1 scan was reclassified from osteoporosis to low bone mineral density, 11 from low to normal bone mineral density, and 15 were unchanged.ConclusionThe impact of an analysis error on participant safety, specifically when the initiation of unnecessary treatment may result, necessitated implementation of a coordinated communication and mitigation plan across all clinical centers to ensure consistent messaging and accurate results are provided to participants and their local care providers. This framework may serve as a resource for othe","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"538-546"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12379604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143971581","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":"From RAGs to riches: Utilizing large language models to write documents for clinical trials.","authors":"Nigel Markey, Ilyass El-Mansouri, Gaetan Rensonnet, Casper van Langen, Christoph Meier","doi":"10.1177/17407745251320806","DOIUrl":"10.1177/17407745251320806","url":null,"abstract":"<p><p>Background/AimsClinical trials require numerous documents to be written: Protocols, consent forms, clinical study reports, and many others. Large language models offer the potential to rapidly generate first-draft versions of these documents; however, there are concerns about the quality of their output. Here, we report an evaluation of how good large language models are at generating sections of one such document, clinical trial protocols.MethodsUsing an off-the-shelf large language model, we generated protocol sections for a broad range of diseases and clinical trial phases. Each of these document sections we assessed across four dimensions: <i>Clinical thinking and logic; Transparency and references; Medical and clinical terminology</i>; and <i>Content relevance and suitability</i>. To improve performance, we used the retrieval-augmented generation method to enhance the large language model with accurate up-to-date information, including regulatory guidance documents and data from ClinicalTrials.gov. Using this retrieval-augmented generation large language model, we regenerated the same protocol sections and assessed them across the same four dimensions.ResultsWe find that the off-the-shelf large language model delivers reasonable results, especially when assessing <i>content relevance</i> and the <i>correct use of medical and clinical terminology</i>, with scores of over 80%. However, the off-the-shelf large language model shows limited performance in <i>clinical thinking and logic</i> and <i>transparency and references</i>, with assessment scores of ≈40% or less. The use of retrieval-augmented generation substantially improves the writing quality of the large language model, with <i>clinical thinking and logic</i> and <i>transparency and references</i> scores increasing to ≈80%. The retrieval-augmented generation method thus greatly improves the practical usability of large language models for clinical trial-related writing.DiscussionOur results suggest that hybrid large language model architectures, such as the retrieval-augmented generation method we utilized, offer strong potential for clinical trial-related writing, including a wide variety of documents. This is potentially transformative, since it addresses several major bottlenecks of drug development.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"626-631"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514403","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 randomized study comparing patient portal and email communications for trial recruitment.","authors":"Jeanette Y Ziegenfuss, Elanadora U Sour, Erica J Roelofs, Jennifer M Vesely, Karen L Margolis, Stephanie A Hooker","doi":"10.1177/17407745251358259","DOIUrl":"10.1177/17407745251358259","url":null,"abstract":"&lt;p&gt;&lt;p&gt;BackgroundRecruitment is a necessary, yet challenging component to clinical trial implementation. Using intentional strategies to meet enrollment goals is important to ensure the recruited sample adequately reflects the intended study population. Outreach via electronic health record (EHR) patient portals is a promising strategy. While identifying potentially eligible individuals in the EHR is largely accepted as effective, little is known about the effectiveness of outreach via EHR compared to outreach via traditional electronic mail (email) communication given equivalent population identification strategies.MethodsThis study was conducted using recruitment data from one of four study locations participating in the LEAP study, a multi-site, double-blind, placebo-controlled trial studying the long-term use of phentermine on weight loss and blood pressure. Between May 2023 and February 2024, 17,989 potentially trial-eligible participants identified using EHR data were randomized to either portal or email recruitment communications. Outreach success was measured at six milestones between starting the self-screener and study randomization. Recruitment rates overall and by demographic subpopulation are reported at each milestone as a percent of total invited and as a percent of previous milestone completers. Multivariate analysis considers the moderating effect of demographics on the relative impact of communication type.ResultsOverall, 6.6% (n = 1191) completed the self-screener and 0.5% (n = 85) were randomized into the LEAP trial. Individuals randomized to patient portal communication were more likely to start the self-screener (Odds Ratio [OR]= 2.4 [2.12, 2.73], p &lt; 0.0001) and complete the subsequent four steps, however there was no significant difference in the percent ultimately randomized into the study (OR = 1.43 [0.93, 2.21], p = 0.10). Moreover, when controlling for completion of the previous step, all subsequent milestone differences were no longer significant. Gender was the only significant moderating factor of all available participant characteristics, with women 2.92 ([2.48, 3.43], p &lt; 0.001) and men 1.72 ([1.41, 2.12], p &lt; 0.001) times more likely to respond to portal messages than email communications.ConclusionsInitial activation in study activities was higher in the patient portal group. Although this impact sustained itself across all but the final study milestone and resulted in absolute larger counts among those randomized to portal messages, there is no evidence that this choice will improve representation in biomedical research or the final study randomization rate overall. Therefore, these findings suggest using the portal strategy may lead to more effort without yield on interim steps by both the research team and the potential participants compared to email. Ultimately, the research team's approach may depend on organizational context and study topic, as some topics do not lend themselves to the less secure nature of","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"597-606"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945739","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":"Hybrid sample size calculations for cluster randomised trials using assurance.","authors":"S Faye Williamson, Svetlana V Tishkovskaya, Kevin J Wilson","doi":"10.1177/17407745241312635","DOIUrl":"10.1177/17407745241312635","url":null,"abstract":"<p><strong>Background/aims: </strong>Sample size determination for cluster randomised trials is challenging because it requires robust estimation of the intra-cluster correlation coefficient. Typically, the sample size is chosen to provide a certain level of power to reject the null hypothesis in a two-sample hypothesis test. This relies on the minimal clinically important difference and estimates for the overall standard deviation, the intra-cluster correlation coefficient and, if cluster sizes are assumed to be unequal, the coefficient of variation of the cluster size. Varying any of these parameters can have a strong effect on the required sample size. In particular, it is very sensitive to small differences in the intra-cluster correlation coefficient. A relevant intra-cluster correlation coefficient estimate is often not available, or the available estimate is imprecise due to being based on studies with low numbers of clusters. If the intra-cluster correlation coefficient value used in the power calculation is far from the unknown true value, this could lead to trials which are substantially over- or under-powered.</p><p><strong>Methods: </strong>In this article, we propose a hybrid approach using Bayesian assurance to determine the sample size for a cluster randomised trial in combination with a frequentist analysis. Assurance is an alternative to traditional power, which incorporates the uncertainty on key parameters through a prior distribution. We suggest specifying prior distributions for the overall standard deviation, intra-cluster correlation coefficient and coefficient of variation of the cluster size, while still utilising the minimal clinically important difference. We illustrate the approach through the design of a cluster randomised trial in post-stroke incontinence and compare the results to those obtained from a standard power calculation.</p><p><strong>Results: </strong>We show that assurance can be used to calculate a sample size based on an elicited prior distribution for the intra-cluster correlation coefficient, whereas a power calculation discards all of the information in the prior except for a single point estimate. Results show that this approach can avoid misspecifying sample sizes when the prior medians for the intra-cluster correlation coefficient are very similar, but the underlying prior distributions exhibit quite different behaviour. Incorporating uncertainty on all three of the nuisance parameters, rather than only on the intra-cluster correlation coefficient, does not notably increase the required sample size.</p><p><strong>Conclusion: </strong>Assurance provides a better understanding of the probability of success of a trial given a particular minimal clinically important difference and can be used instead of power to produce sample sizes that are more robust to parameter uncertainty. This is especially useful when there is difficulty obtaining reliable parameter estimates.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"517-526"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398464","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}