Clinical Trials最新文献

{"title":"How to be SMART in oncology: A practical framework to assess the contribution of phase using sequential multiple adaptive randomized treatment designs.","authors":"Kristine R Broglio, Elizabeth F Krakow, Erica E M Moodie","doi":"10.1177/17407745261471462","DOIUrl":"https://doi.org/10.1177/17407745261471462","url":null,"abstract":"<p><strong>Background: </strong>Sequential multiple adaptive randomized treatment designs can generate registrational-quality evidence for the contribution of phase in perioperative oncology by preserving randomized comparisons and intent-to-treat estimation while improving efficiency relative to traditional three-arm trials, and yet they remain underappreciated. We present a sequential multiple adaptive randomized treatment design framework for perioperative regimens with neoadjuvant and adjuvant phases, motivated by perioperative immunotherapy in resectable non-small-cell lung cancer, where the benefit of neoadjuvant therapy is hypothesized to be greater than that of adjuvant therapy.</p><p><strong>Methods: </strong>We define analyses for perioperative-versus-control, neoadjuvant-only-versus-control, and contribution of phase (incremental adjuvant benefit). We specify intent-to-treat analysis sets and describe unbiased estimation.</p><p><strong>Results: </strong>In a hypothetical trial calibrated to a perioperative setting, a sequential multiple adaptive randomized treatment design increases the number of events for regimen-versus-control comparisons and isolates a randomized comparison for the contribution of phase at the second randomization. Compared with a traditional multi-arm design, simulations demonstrate controlled family-wise type I error (<0.05) and higher power.</p><p><strong>Conclusions: </strong>Sequential multiple adaptive randomized treatment designs provide a principled, practical approach to within-trial contribution of phase in confirmatory oncology. Sequential multiple adaptive randomized treatment designs offer an efficient approach that addresses regulatory concerns and provides valid and robust inference. Because sequential multiple adaptive randomized treatment designs formalize the iterative nature of clinical decision-making, they are uniquely poised to explicitly answer questions related to overtreatment or undertreatment.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261471462"},"PeriodicalIF":2.4,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148863856","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 practical application for the efficient use of historical data in randomized controlled design using systematic review data.","authors":"Tim J Schouten, Andrea Gabrio, Jochen W L Cals, Mark Spigt","doi":"10.1177/17407745261467982","DOIUrl":"https://doi.org/10.1177/17407745261467982","url":null,"abstract":"<p><strong>Introduction: </strong>Randomized controlled trials are the gold standard for evaluating interventions but are resource-intensive and impose considerable burden on participants. Historical control designs, which incorporate data from previous trials into new analyses, could reduce sample size requirements, but their practical application is limited. This study explored the feasibility of constructing a historical control using the Bayesian meta-analytic predictive approach based on data from a systematic review of metformin in type 2 diabetes mellitus.</p><p><strong>Methods: </strong>We extracted baseline and end point glycated hemoglobin values and participant characteristics from 25 randomized trials (36 comparisons). Using the RBesT package in the R software, meta-analytic predictive priors were constructed, and their contribution to new data was quantified using the effective sample size metric. Between-trial heterogeneity (τ) was initially set to the default conservative value (0.5) and subsequently estimated and replaced in the analysis using the output from the metafor package based on the reported study-level variances. Additional stratified analyses based on follow-up duration and publication year were conducted.</p><p><strong>Results: </strong>Initial models yielded low effective sample size values (1-4), indicating minimal information borrowing from historical data. The presence of a high level of heterogeneity (τ = 0.55-0.85) appeared to limit the priors' influence. Further investigation into a stricter data set showed that four outlier studies reduced the estimated τ from the data to 0.0935, which increased effective sample size from 18 (conservative model) to 52 (data-derived model), while estimated treatment effects remained stable (-1.34% to -1.36% glycated hemoglobin).</p><p><strong>Discussion: </strong>Between-trial heterogeneity (τ) estimation critically influences the informativeness of meta-analytic predictive-based historical control analysis. Despite having 25 trials with the same condition, the effective sample size was very low. Data-driven τ estimation substantially increased effective sample size without altering the treatment effect estimates and could represent a valid alternative to historical controls in clinical trial design compared with default conservative choices.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261467982"},"PeriodicalIF":2.4,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148857000","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":"Effectiveness of the recruitment strategies used in the Baby-Feed Online Trial, a trial to test an educational web application for caregivers to prevent excessive weight gain in infants.","authors":"Cristina Palacios, Alayne Gatto, Gabriella Reingevurts, Mariana Leon, Sophia Acosta, Colleen Sloan","doi":"10.1177/17407745261463614","DOIUrl":"10.1177/17407745261463614","url":null,"abstract":"<p><strong>Background/aims: </strong>To analyze the effectiveness of recruitment methods in the Baby-Feed Online Trial.</p><p><strong>Methods: </strong>This randomized clinical trial evaluated an online educational intervention for caregivers to monitor and improve their infants' diets and growth. Caregivers of infants aged <4 months living in the United States were recruited using various sources, including \"Traditional\" (flyers and snowball) and \"Online\" (social media and blast emails) methods. All methods led to an online pre-screening survey using Qualtrics, a secure, web-based survey platform, to assess eligibility. We analyzed the number of pre-screenings completed by recruitment method, their socio-demographics, and issues with bots. The Florida International University-Institutional Review Board approved the study.</p><p><strong>Results: </strong>Of the 1213 pre-screenings received, 84% were identified as bots or spam, prompting the implementation of additional security tools in the online pre-screener and a verification call. Within 6 months, 155 individuals were enrolled; 51% came from \"Traditional\" strategies and 49% from \"Online\" strategies. The most effective methods were incentivized snowball referrals from enrolled participants, other strategies from the Florida International University research team (referrals, emails, social media posts), and posts in Facebook groups. Participants were recruited from 24 states, with the largest proportion coming from Florida (N = 50; 32.3%). From enrolled participants, 95.5% were mothers aged 34.2 (5.0) years, 16% were participants of the Women, Infant, and Children Program, 65.2% were White, 25.2% were Hispanics, 83.2% had a bachelor's degree or higher, and the mean parental body mass index was 27.3 (4.6) kg/m². The infant's age at enrollment was 2.2 (1.1) months. No significant differences were observed between the \"Traditional\" and \"Online\" recruitment methods in any socio-demographic characteristics.</p><p><strong>Conclusion: </strong>Both recruitment strategies were crucial in recruiting the required sample within a short period with no socio-demographic differences. The incentivized snowball referrals proved to be the most successful strategy. Although a critical challenge was the substantial number of bots, our recruitment plan was effective in recruiting caregivers. However, these strategies were effective in recruiting a highly educated group.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261463614"},"PeriodicalIF":2.4,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148668662","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":"Leveraging generative AI to transform statistical analysis plan authoring in clinical trials.","authors":"Rogier Landman, Birol Emir, Richard Zhang, Giannis Manousaridis, Yi-Chien Lee, Lili Jiang, Gordon Siu, Feng Dai, Griffith Bell, Alexandra Thiry, Akhil Shekkari, Moumita Dasandhi, Amy Lauren Ashworth, Donal Nielson Gorman, Anna Plotka, Allison Wehr, Colombe Chappey, Subha Madhavan, Sheraz Khan","doi":"10.1177/17407745261422365","DOIUrl":"10.1177/17407745261422365","url":null,"abstract":"&lt;p&gt;&lt;p&gt;BackgroundStatistical analysis plans are critical regulatory documents that define the statistical methodology, objectives, and data-handling procedures for clinical trials. The traditional process of statistical analysis plan development is resource-intensive, typically spanning 4-6 weeks, and is increasingly complicated by evolving regulatory requirements and complex trial designs such as adaptive studies and real-world evidence investigations. The emergence of generative artificial intelligence presents an opportunity to streamline this workflow; however, applying AI in regulated environments requires rigorous control over accuracy and consistency.MethodsTo address these challenges, we designed and implemented a generative artificial intelligence-based solution tailored to the automated drafting of statistical analysis plans. The system utilizes a knowledge graph and vector database architecture to parse clinical protocols and map content to study-specific templates. We employed a hybrid generation strategy featuring four distinct modes: verbatim copying, summarization, standard text with dynamic variable insertion, and de novo text generation via prompt engineering. The solution was deployed as both a web application and a Microsoft Word add-in and was evaluated across a diverse portfolio of trial designs, including interventional, non-interventional, clinical pharmacology, and oncology studies.ResultsThe deployment of the solution yielded a transformative reduction in drafting time, creating &lt;i&gt;first drafts&lt;/i&gt; in an average of 1.0-3.4 min across 71 statistical analysis plans, compared to the traditional timeline of 1-2 days. Subject matter expert evaluations rated the outputs between 3.6 and 4.2 on a five-point Likert-type scale, indicating moderate to high quality. Furthermore, in comparisons against manually authored statistical analysis plans, a Large Language Model-assisted evaluation demonstrated substantial preservation of meaning across corresponding sections, yielding an average semantic similarity score of 0.75. Automated standardization features ensured a high degree of consistency across diverse statistical analysis plan documents. This approach enables the management of expansive statistical analysis plan portfolios and supports a broad spectrum of trial designs, including adaptive studies and real-world evidence investigations.ConclusionThe application of generative artificial intelligence to statistical analysis plan authoring represents a substantive advancement for clinical research operations, delivering marked improvements in drafting speed and documentation consistency. Beyond immediate efficiency gains, standardized automated text generation may benefit downstream processes such as statistical programming and clinical study report drafting. Future work should focus on strengthening automated quality assurance, supporting amendments with auditable revision handling, and further integrating artificial intelligence-as","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"383-392"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147347323","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":"Comparing meta-analysis and linear mixed model-based approaches for the analysis of continuous outcomes from batched stepped wedge trials.","authors":"Dixon Orlando, Rhys Bowden, Kelsey L Grantham, Joanne E McKenzie, Andrew B Forbes, Jessica Kasza","doi":"10.1177/17407745261429957","DOIUrl":"10.1177/17407745261429957","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Stepped wedge designs are a specific type of cluster randomised trial design that requires all clusters to start and end the study at the same time. Batched stepped wedge designs are a variant that allows clusters to commence the study in batches instead of all at once. Since different batches of clusters may have different characteristics, it is possible that the treatment effect differs across batches. Here, we explore different approaches for accommodating this treatment effect heterogeneity when analysing data from a batched stepped wedge design where each participant provides a single outcome measurement: linear mixed models with a random coefficient for the intervention for each batch of clusters with estimation via restricted maximum likelihood; and meta-analysis approaches that pool the batch-specific treatment effect estimates using fixed- or random-effects meta-analysis approaches. In many batched stepped wedge designs, the number of batches will be low (e.g. 2); it is unclear what impact the small number of batches may have on bias and confidence interval coverage of these approaches.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We simulated continuous outcome data from a range of batched stepped wedge designs with two and five batches, different within-cluster correlation structures (exchangeable, block-exchangeable), with and without period effects shared across batches, and with and without treatment effect heterogeneity across batches. Using the 'lme4' and 'meta' packages in R 4.4.1, we analysed each simulated data set using both linear mixed effects models (with and without treatment effect heterogeneity) and a range of meta-analysis approaches. We assessed the relative performance of all methods to estimate the true treatment effect using rates of convergence, bias, confidence interval coverage, and empirical standard error.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Treatment effect estimates from all meta-analysis approaches were unbiased. When period effects were incorrectly specified, treatment effects from linear mixed effects models were biased; when correctly specified, treatment effect estimates from these models were unbiased. Confidence interval coverage was similar for all random-effects meta-analysis approaches and close to the nominal level. For fixed-effect meta-analysis and linear mixed models, coverage depended on whether treatment effect heterogeneity was present.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;When batched stepped wedge trials include a small number of batches (two and five batches), linear mixed models provide unbiased estimates of treatment effects when correctly specified, but exhibit under-coverage of confidence intervals when treatment effect heterogeneity is present. Random-effects meta-analysis approaches provide unbiased estimates and nominal confidence interval coverage whether or not period or treatment effects vary across batches, but can yield excessively wide confidence intervals. When b","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"393-403"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147493427","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":"Developing a platform protocol for clinical trials evaluating interventions that target proposed mechanisms of Long COVID: RECOVER-VITAL.","authors":"Kanecia O Zimmerman, Richard Whitley, Sean O'Brien, David R Walt, Bruce D Levy, Christine Maughan, Marta Cerda, Rachel Olson, Lucinda Bateman, Cyndya A Shibao, Barry Make, David Knopman, Susan Redline, Leonard A Jason, Mehul S Suthar, Phillip Low, Tracy L Nolen, Craig Reist, Lisa Berdan, Lindsey R Baden","doi":"10.1177/17407745261422437","DOIUrl":"10.1177/17407745261422437","url":null,"abstract":"<p><strong>Background: </strong>Long COVID, the chronic sequela of SARS-CoV-2 infection, has affected millions of people worldwide. However, its pathogenesis and treatment remain unknown. To address this critical gap, the National Institutes of Health (NIH) developed the Researching COVID to Enhance Recovery (RECOVER) Initiative that included the Viral Persistence and Reactivation, and Immune Dysregulation (RECOVER-VITAL) study as one of the first of five NIH-sponsored, integrated platform protocols to support the rigorous and rapid investigation of potential interventions for Long COVID.</p><p><strong>Methods: </strong>Experts across academia, the NIH, and the community of patients and caregivers were brought together to design the RECOVER-VITAL protocol. We present the challenges and rationale underpinning critical components of this protocol and provide evidence for use of the platform for efficient execution of current and future Long COVID clinical trials. Facets of the RECOVER-VITAL protocol, including inclusion criteria, intervention groups, study procedures, investigation of biomarkers, and endpoints, were carefully crafted to advance the current and future science and operations of Long COVID clinical trials, particularly in the absence of existing data. The initial trial within the RECOVER-VITAL platform evaluated two durations of an antiviral drug, nirmatrelvir/ritonavir, to test the proposed etiology of viral persistence on the Long COVID symptom clusters of autonomic dysfunction, cognitive dysfunction, and exercise intolerance. The primary outcome measures were patient-reported, and secondary outcomes included performance-based measures for each of the symptom clusters of interest.</p><p><strong>Conclusions: </strong>The RECOVER-VITAL platform protocol has substantial implications for the design and conduct of future Long COVID clinical trials.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"413-420"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147364432","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":"Additional crossovers in cluster randomised crossover trials do not always increase statistical power.","authors":"K M Tanvir, Andrew B Forbes, Kelsey L Grantham, Jessica Kasza","doi":"10.1177/17407745261431140","DOIUrl":"10.1177/17407745261431140","url":null,"abstract":"<p><strong>Background: </strong>Cluster randomised crossover designs (CRXOs) are a powerful type of longitudinal cluster randomised trial in which all participating clusters switch between two treatment conditions. \"Multiple-period\" CRXOs divide the trial duration into a number of periods of equal length and can allow for multiple crossovers between treatment conditions. It can be assumed that increasing the number of crossovers leads to an increase in statistical power. We investigate whether this is true for standard correlation structures, comparing CRXO designs with equal numbers of clusters, participants, and periods but differing in the number of crossovers, considering continuous outcomes.</p><p><strong>Methods: </strong>We consider the formula for the variance of the treatment effect estimator for multiple-period CRXOs under exchangeable and block-exchangeable within-cluster correlation structures, assuming equal cluster-period sizes and different patterns of treatment conditions in the treatment sequences varying in the number of crossovers. We also conduct a simulation study to compare the statistical power between multiple-period CRXO designs with different numbers of crossovers that share the same number and duration of periods and the same number of participants in each cluster period.</p><p><strong>Results: </strong>Under exchangeable and block-exchangeable correlation structures and equal cluster-period sizes, the number of crossovers in the treatment sequences does not impact study power, provided the design is balanced in terms of the number of periods and clusters implementing each condition.</p><p><strong>Conclusions: </strong>When an exchangeable or block-exchangeable within-cluster correlation structure and a time-invariant effect of treatment are assumed, power calculations for CRXO designs are invariant to the specific ordering of treatment conditions. In particular, a CRXO design with additional crossovers does not lead to increased statistical power compared to a CRXO design with just one crossover for these within-cluster correlation structures. Further work is required to investigate the utility of multiple crossovers in situations where the treatment effect varies over time.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"447-452"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572684","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":"Multiple imputation in clinical trials - what difference does it make? A systematic review of the impact of multiple imputation on treatment effect estimation.","authors":"Thomas R Sullivan, Katherine J Lee, Jana M Bednarz, Lisa N Yelland","doi":"10.1177/17407745261422359","DOIUrl":"10.1177/17407745261422359","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;Multiple imputation is often recommended over complete case analysis for handling missing data in clinical trials due to its ability to recover information from participants with incomplete data. While multiple imputation is generally held to be more efficient than complete case analysis, its planned use in clinical trials is typically not considered during sample size estimation. The standard approach of inflating the sample size for anticipated loss to follow-up is applicable for complete case analysis but could lead to excess power and hence inefficient resource use should multiple imputation be planned for analysis. In this article, we systematically reviewed published clinical trials with the aim of quantifying the precision advantages of multiple imputation over complete case analysis in treatment effect estimation, hence informing sample size planning for future trials.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a targeted review of clinical trials published between January 2019 and December 2023 in &lt;i&gt;Lancet&lt;/i&gt;, &lt;i&gt;The BMJ&lt;/i&gt;, &lt;i&gt;Journal of the American Medical Association&lt;/i&gt; and &lt;i&gt;New England Journal of Medicine&lt;/i&gt;. Clinical trials were eligible for inclusion if point and variance estimates for the effect of treatment on a primary efficacy or safety outcome could be determined for both multiple imputation and complete case analysis. The design effect due to multiple imputation was calculated as the variance of the treatment effect estimate using multiple imputation divided by the corresponding variance using complete case analysis. As a supplementary analysis, we also conducted an untargeted review of other journals by searching in PubMed for clinical trials with the keywords 'imputation' or 'imputed' in their title or abstract.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The targeted search identified 547 articles, of which 59 satisfied eligibility criteria. Included trials tended to be large in size (median 653 participants) and reported a median of 8.6% missing data in the complete case analysis of the primary outcome (range 0.4%-30.5%). Multiple imputation was most frequently applied using chained equations under a missing at random assumption, with auxiliary variables included in the imputation model in most trials. The median design effect due to multiple imputation was 1.00 in both unadjusted (n = 15 trials) and covariate-adjusted analyses (n = 46 trials), suggesting multiple imputation typically was not offering precision advantages over complete case analysis. Similar design effects were observed in the untargeted review (median 0.96 and 1.01 for unadjusted and covariate-adjusted analyses), despite higher rates of missing data overall (median 15.7%, n = 49 trials).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;Multiple imputation did not consistently lead to more precise treatment effect estimates than complete case analysis in the trials included in the review. Findings should not be construed as an argumen","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"430-438"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147347362","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":"Premarket and postmarket real-world evidence studies supporting U.S. Food and Drug Administration regulatory decision-making, 2016-2024.","authors":"Louis Y Li, Reshma Ramachandran, Joseph S Ross, Joshua D Wallach","doi":"10.1177/17407745251415190","DOIUrl":"10.1177/17407745251415190","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;There is growing interest in leveraging real-world data, such as electronic health records, administrative claims data, and patient registries, to generate real-world evidence studies that support the U.S. Food and Drug Administration's premarket and postmarket regulatory determinations of effectiveness and/or safety for novel therapeutics. We examined the frequency and characteristics of real-world evidence studies used by the U.S. Food and Drug Administration to support premarket determinations of effectiveness and/or safety, as well as those required or requested by the U.S. Food and Drug Administration to be conducted postmarket after approval.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We identified all novel therapeutics approved by the U.S. Food and Drug Administration between 2016 and 2024, using action packages from the Drugs@FDA database. Product labels, approval letters, and review documents were used to identify real-world evidence studies supporting premarket determinations of effectiveness and/or safety, as well as all postmarketing requirements or commitments outlined at the time of approval. Outcomes included the number of novel therapeutics approved with premarket and/or postmarket real-world evidence studies and characteristics of these studies, including study design, data source, and primary objectives.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;From 2016 to 2024, the U.S. Food and Drug Administration approved 400 novel therapeutics for 543 indications, of which 43 (10.8%) had at least one real-world evidence study that supported premarket determinations of effectiveness and/or safety (64 unique studies), and 138 (34.5%) had at least one real-world evidence study required or requested by the U.S. Food and Drug Administration to be conducted postmarket after approval (208 unique studies). Among the 64 unique premarket real-world evidence studies, the most common study designs were non-interventional (observational) studies (35, 54.7%) and externally controlled trials (17, 26.6%); 38 (59.4%) studies utilized electronic health or medical records, and 47 (73.4%) provided evidence on effectiveness. Among the 208 unique postmarket real-world evidence studies, the most common study design was non-interventional (observational) studies (159, 76.4%); 61 (29.3%) studies identified registries as the proposed data source, and 197 (94.7%) were designed to provide evidence on safety alone. The proportion of therapeutics approved with at least one postmarket real-world evidence study increased over time from 2 of 20 (10.0%) in 2016 to 23 of 47 (48.9%) in 2024; however, only 7 (3.4%) of these studies were classified by the U.S. Food and Drug Administration as fulfilled or submitted as of May 2025.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Real-world evidence studies are infrequently used to support the U.S. Food and Drug Administration's premarket determinations of effectiveness and/or safety but have been increasingly required or re","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"363-374"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12900038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146124124","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":"Response-adaptive randomization with imperfect intermediate endpoints.","authors":"Yousra Kherabi, Michael A Proschan, Lori E Dodd","doi":"10.1177/17407745261415792","DOIUrl":"10.1177/17407745261415792","url":null,"abstract":"<p><strong>Background: </strong>Response-adaptive randomization is controversial even in the best circumstances when based on a quickly determined primary outcome. In disease settings in which the primary outcome requires long follow-up, an intermediate endpoint may be chosen to update randomization allocations. The aim of our study is to evaluate the impact of response-adaptive randomization applied to an imperfect intermediate endpoint. We use tuberculosis trials as the motivating example.</p><p><strong>Methods: </strong>We simulated a response-adaptive randomization design, adapting randomization allocations using an imperfect intermediate endpoint, in a superiority trial of two experimental regimens and one control arm. The primary study outcome was treatment success after 73 weeks from randomization; the intermediate endpoint was culture conversion at 8 weeks. We compared different sensitivity (Se) and specificity (Spe) scenarios for the intermediate endpoint, while varying the true treatment efficacy. We evaluated the performance of response-adaptive randomization to achieve its primary goal of allocating more participants to the better arm and the impact of time-trends on type I error rate.</p><p><strong>Results: </strong>Even in an ideal state of perfect accuracy (i.e. intermediate endpoint with Se = 100% and Spe = 100%), response-adaptive randomization did not always live up to its main purpose of allocating more patients to the better arm. Lower accuracy of the intermediate endpoint leads to greater divergence from the goal of more allocations to the better arm. The larger the difference in treatment efficacy between the arms, the more striking the impact of an intermediate endpoint with poor diagnostic accuracy. Time-trends inflate the type I error rate, and while stratified tests can correct this, they do so at the cost of a power loss. Allocating more patients to the worst arm increases power for comparisons with this arm but reduces power for comparisons of the best arm to control.</p><p><strong>Conclusion: </strong>Given the objective of evaluating several new therapeutic regimens in a timely manner, response-adaptive randomization is tempting. However, it requires at least reliance on highly accurate intermediate endpoints, which are still no guarantee of response-adaptive randomization's trustworthiness.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"375-382"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147282663","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}