Clinical Trials最新文献

{"title":"An open-source SQL database schema for integrated clinical and translational data management in clinical trials.","authors":"Umar Niazi, Charlotte Stuart, Patricia Soares, Vincent Foure, Gareth Griffiths","doi":"10.1177/17407745241304331","DOIUrl":"10.1177/17407745241304331","url":null,"abstract":"<p><p>Unlocking the power of personalised medicine in oncology hinges on the integration of clinical trial data with translational data (i.e. biospecimen-derived molecular information). This combined analysis allows researchers to tailor treatments to a patient's unique biological makeup. However, current practices within UK Clinical Trials Units present challenges. While clinical data are held in standardised formats, translational data are complex, diverse, and requires specialised storage. This disparity in format creates significant hurdles for researchers aiming to curate, integrate and analyse these datasets effectively. This article proposes a novel solution: an open-source SQL database schema designed specifically for the needs of academic trial units. Inspired by Cancer Research UK's commitment to open data sharing and exemplified by the Southampton Clinical Trials Unit's CONFIRM trial (with over 150,000 clinical data points), this schema offers a cost-effective and practical 'middle ground' between raw data and expensive Secure Data Environments/Trusted Research Environments. By acting as a central hub for both clinical and translational data, the schema facilitates seamless data sharing and analysis. Researchers gain a holistic view of trials, enabling exploration of connections between clinical observations and the molecular underpinnings of treatment response. Detailed instructions for setting up the database are provided. The open-source nature and straightforward design ensure ease of implementation and affordability, while robust security measures safeguard sensitive data. We further showcase how researchers can leverage popular statistical software like R to directly query the database. This approach fosters collaboration within the academic discovery community, ultimately accelerating progress towards personalised cancer therapies.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"374-377"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884787","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 framework for sequential monitoring of individual N-of-1 trials and combining results across a series of sequentially monitored N-of-1 trials.","authors":"Subodh Selukar, David K Prince, Susanne May","doi":"10.1177/17407745241304284","DOIUrl":"10.1177/17407745241304284","url":null,"abstract":"<p><strong>Background: </strong>N-of-1 trials compare two or more treatment options for a single participant. These trials have been used to study options for chronic conditions such as arthritis and attention deficit hyperactivity disorder. In addition, they have been suggested as a means to study interventions in rare populations that may not be tractable to include in standard clinical trials, such as treatment options for HIV-positive patients in need of organ transplant. Sequential monitoring of accruing data has been well-studied in traditional clinical trials, but these methods have not yet been implemented in N-of-1 trials. However, the option to validly stop an N-of-1 trial early could deliver faster decisions that could directly improve the patient's health.</p><p><strong>Methods: </strong>In this work, we propose and evaluate a framework to (1) facilitate sequential monitoring in individual N-of-1 trials with a continuous outcome and (2) combine results across a series of already-completed sequentially monitored N-of-1 trials. By employing the block structure common to N-of-1 trials, we suggest that existing approaches to sequential monitoring may be employed when data from one N-of-1 trial are analyzed with a linear mixed-effects model. To combine results across a series of already-completed sequentially monitored N-of-1 trials, we propose combining the naive estimates from constituent trials in a random-effects model with inverse-variance weighting. We evaluate these proposals via simulation.</p><p><strong>Results: </strong>We find that type 1 error can be substantially inflated for N-of-1 trials with a small number of planned blocks but can reach the nominal rate for trials with more planned blocks or those with larger numbers of periods per block or by using a <math><mrow><mi>t</mi></mrow></math>-value correction. For those settings with acceptable type 1 error, sequential monitoring results in similar power and on average earlier stopping compared with trials with no sequential monitoring. And, as expected, we find that including a larger number of constituent trials in a series reduces the mean-squared error of the combined point estimator.</p><p><strong>Conclusion: </strong>Under suitable design considerations, our proposed framework for sequential monitoring can support clinicians in providing important decisions earlier, on average, for patients engaged in N-of-1 trials.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"257-266"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12094905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913822","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":"Concordance between clinical trial data use request proposals and corresponding publications: A cross-sectional study.","authors":"Enrique Vazquez, Joseph S Ross, Cary P Gross, Karla Childers, Stephen Bamford, Jessica D Ritchie, Joanne Waldstreicher, Harlan M Krumholz, Joshua D Wallach","doi":"10.1177/17407745241304355","DOIUrl":"10.1177/17407745241304355","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Background/AimsThe reuse of clinical trial data available through data-sharing platforms has grown over the past decade. Several prominent clinical data-sharing platforms require researchers to submit formal research proposals before granting data access, providing an opportunity to evaluate how published analyses compare with initially proposed aims. We evaluated the concordance between the included trials, study objectives, endpoints, and statistical methods specified in researchers' clinical trial data use request proposals to four clinical data-sharing platforms and their corresponding publications.MethodsWe identified all unique data request proposals with at least one corresponding peer-reviewed publication as of 31 March 2023 on four prominent clinical trial data sharing request platforms (Vivli, ClinicalStudyDataRequest.com, the Yale Open Data Access Project, and Supporting Open Access to Researchers-Bristol Myers Squibb). When data requests had multiple publications, we treated each publication-request pair as a unit. For each pair, the trials requested and analyzed were classified as fully concordant, discordant, or unclear, whereas the study objectives, primary and secondary endpoints, and statistical methods were classified as fully concordant, partially concordant, discordant, or unclear. For Vivli, ClinicalStudyDataRequest.com, and Supporting Open Access to Researchers-Bristol Myers Squibb, endpoints of publication-request pairs were not compared because the data request proposals on these platforms do not consistently report this information.ResultsOf 117 Vivli publication-request pairs, 76 (65.0%) were fully concordant for the trials requested and analyzed, 61 (52.1%) for study objectives, and 57 (48.7%) for statistical methods; 35 (29.9%) pairs were fully concordant across the 3 characteristics reported by all platforms. Of 106 ClinicalStudyDataRequest.com publication-request pairs, 66 (62.3%) were fully concordant for the trials requested and analyzed, 41 (38.7%) for study objectives, and 35 (33.0%) for statistical methods; 20 (18.9%) pairs were fully concordant across the 3 characteristics. Of 65 Yale Open Data Access Project publication-request pairs, 35 (53.8%) were fully concordant for the trials requested and analyzed, 44 (67.7%) for primary study objectives, and 25 (38.5%) for statistical methods; 15 (23.1%) pairs were fully concordant across the 3 characteristics. In addition, 26 (40.0%) and 2 (3.1%) Yale Open Data Access Project publication-request pairs were concordant for primary and secondary endpoints, respectively, such that only one (1.5%) Yale Open Data Access Project publication-request pair was fully concordant across all five characteristics reported. Of three Supporting Open Access to Researchers-Bristol Myers Squibb publication-request pairs, one (33.3%) was fully concordant for the trials requested and analyzed, two (66.6%) for primary study objectives, and two (66.6%) for statistical methods; one (33.","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"22 3","pages":"279-288"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12095927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109862","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":"Sequential monitoring of time-to-event safety endpoints in clinical trials.","authors":"Michael J Martens, Qinghua Lian, Nancy L Geller, Eric S Leifer, Brent R Logan","doi":"10.1177/17407745241304119","DOIUrl":"10.1177/17407745241304119","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Background/aimsSafety monitoring is a crucial requirement for Phase II and III clinical trials. To protect patients from toxicity risk, stopping rules may be implemented that will halt the study if an unexpectedly high number of events occur. These rules are constructed using statistical procedures that typically treat the toxicity data as binary occurrences. Because the exact dates of toxicities are often available, a strategy that handles these as time-to-event data may offer higher power and require less calendar time to identify excess risk. This work investigates several statistical methods for monitoring safety events as time-to-event endpoints and illustrates our R software package for designing and evaluating these procedures.MethodsThe performance metrics of safety stopping rules derived from Wang-Tsiatis tests, Bayesian Gamma-Poisson models, and sequential probability ratio tests are evaluated and contrasted in Phase II and III trial scenarios. We developed a publicly available R package \"stoppingrule\" for designing and assessing these stopping rules whose utility is illustrated through the design of a stopping rule for Blood and Marrow Transplant Clinical Trials Network 1204 (National Clinical Trial number NCT01998633), a multicenter, Phase II, single-arm trial that assessed the efficacy and safety of bone marrow transplant for the treatment of hemophagocytic lymphohistiocytosis and primary immune deficiencies.ResultsAs seen previously in group sequential testing settings, rules with strict stopping criteria early in a study tend to have more lenient stopping criteria late in the trial. Consequently, methods with aggressive early monitoring, such as Gamma-Poisson models with weak priors and certain choices of truncated sequential probability ratio tests, usually yield a smaller number of toxicities and lower power than ones that are more permissive at early stages, such as Gamma-Poisson models with strong priors and the O'Brien-Fleming test. The Pocock test and maximized sequential probability ratio test performed contrary to these trends, however, exhibiting both diminished power and higher numbers of toxicities than other methods due to their extremely aggressive early stopping criteria, failing to reserve adequate power to identify safety issues beyond the start of the study. In contrast to binary toxicity approaches, our time-to-event methods offer meaningful reductions in expected toxicities of up to 20% across scenarios considered.ConclusionSafety monitoring procedures aim to guard study participants from being exposed to and suffering toxicity from unsafe treatments. Toward this end, we recommend considering the time-to-event-oriented Gamma-Poisson model-weak prior model or truncated sequential probability ratio test for constructing safety stopping rules, as they performed the best in minimizing the number of toxicities in our investigations. Our R package \"stoppingrule\" offers procedures for creating and assessing stoppi","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"22 3","pages":"267-278"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12096354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109871","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":"Central statistical monitoring in clinical trial management: A scoping review.","authors":"Maciej Fronc, Michał Jakubczyk, Sharon B Love, Susan Talbot, Timothy Rolfe","doi":"10.1177/17407745241304059","DOIUrl":"10.1177/17407745241304059","url":null,"abstract":"<p><strong>Background: </strong>Clinical trials handle a huge amount of data which can be used during the trial to improve the ongoing study conduct. It is suggested by regulators to implement the remote approach to evaluate clinical trials by analysing collected data. Central statistical monitoring helps to achieve that by employing quantitative methods, the results of which are a basis for decision-making on quality issues.</p><p><strong>Methods: </strong>This article presents a scoping review which is based on a systematic and iterative approach to identify and synthesise literature on central statistical monitoring methodology. In particular, we investigated the decision-making processes (with emphasis on quality issues) of central statistical monitoring methodology and its place in the clinical trial workflow. We reviewed papers published over the last 10 years in two databases (Scopus and Web of Science) with a focus on data mining algorithms of central statistical monitoring and its benefit to the quality of trials.</p><p><strong>Results: </strong>As a result, 24 scientific papers were selected for this review, and they consider central statistical monitoring at two levels. First, the perspective of the central statistical monitoring process and its location in the study conduct in terms of quality issues. Second, central statistical monitoring methods categorised into practices applied in the industry, and innovative methods in development. The established methods are discussed through the prism of categories of their usage. In turn, the innovations refer to either research on new methods or extensions to existing ones.</p><p><strong>Discussion: </strong>Our review suggests directions for further research into central statistical monitoring methodology - including increased application of multivariate analysis and using advanced distance metrics - and guidance on how central statistical monitoring operates in response to regulators' requirements.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"342-351"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7617700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913824","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":"Impact of differences between interim and post-interim analysis populations on outcomes of a group sequential trial: Example of the MOVe-OUT study.","authors":"Yoseph Caraco, Matthew G Johnson, Joseph A Chiarappa, Brian M Maas, Julie A Stone, Matthew L Rizk, Mary Vesnesky, Julie M Strizki, Angela Williams-Diaz, Michelle L Brown, Patricia Carmelitano, Hong Wan, Alison Pedley, Akshita Chawla, Dominik J Wolf, Jay A Grobler, Amanda Paschke, Carisa De Anda","doi":"10.1177/17407745251313925","DOIUrl":"10.1177/17407745251313925","url":null,"abstract":"&lt;p&gt;&lt;p&gt;BackgroundPre-specified interim analyses allow for more timely evaluation of efficacy or futility, potentially accelerating decision-making on an investigational intervention. In such an analysis, the randomized, double-blind MOVe-OUT trial demonstrated superiority of molnupiravir over placebo for outpatient treatment of COVID-19 in high-risk patients. In the full analysis population, the point estimate of the treatment difference in the primary endpoint was notably lower than at the interim analysis. We conducted a comprehensive assessment to investigate this unexpected difference in treatment effect size, with the goal of informing future clinical research evaluating treatments for rapidly evolving infectious diseases.MethodsThe modified intention-to-treat population of the MOVe-OUT trial was divided into an interim analysis cohort (i.e. all participants included in the interim analysis; prospectively defined) and a post-interim analysis cohort (i.e. all remaining participants; retrospectively defined). Baseline characteristics (including many well-established prognostic factors for disease progression), clinical outcomes, and virologic outcomes were retrospectively evaluated. The impact of changes in baseline characteristics over time was explored using logistic regression modeling and simulations.ResultsBaseline characteristics were well-balanced between arms overall. However, between- and within-arm differences in known prognostic baseline factors (e.g. comorbidities, SARS-CoV-2 viral load, and anti-SARS-CoV-2 antibody status) were observed for the interim and post-interim analysis cohorts. For the individual factors, these differences were generally minor and otherwise not notable; as the trial progressed, however, these shifts in combination increasingly favored the placebo arm across most of the evaluated factors in the post-interim cohort. Model-based simulations confirmed that the reduction in effect size could be accounted for by these longitudinal trends toward a lower-risk study population among placebo participants. Infectivity and viral load data confirmed that molnupiravir's antiviral activity was consistent across both cohorts, which were heavily dominated by different viral clades (reflecting the rapid SARS-CoV-2 evolution).DiscussionThe cumulative effect of randomly occurring minor differences in prognostic baseline characteristics within and between arms over time, rather than virologic factors such as reduced activity of molnupiravir against evolving variants, likely impacted the observed outcomes. Our results have broader implications for group sequential trials seeking to evaluate treatments for rapidly emerging pathogens. During dynamic epidemic or pandemic conditions, adaptive trials should be designed and interpreted especially carefully, considering that they will likely rapidly enroll a large post-interim overrun population and that even small longitudinal shifts across multiple baseline variables can disproporti","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"312-324"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143536668","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":"Impact of correlation structure on sample size requirements of statistical methods for multiple binary outcomes: A simulation study.","authors":"Kanako Fuyama, Kentaro Sakamaki, Kohei Uemura, Isao Yokota","doi":"10.1177/17407745241304706","DOIUrl":"10.1177/17407745241304706","url":null,"abstract":"<p><p>BackgroundIn randomized clinical trials, multiple-testing procedures, composite endpoints, and prioritized outcome approaches are increasingly used to analyze multiple binary outcomes. Previous studies have shown that correlations between outcomes influence their sample size requirements. Although sample size is an important factor affecting the choice of statistical methods, the power and required sample sizes of methods for analyzing multiple binary outcomes have yet to be compared under the influence of outcome correlations.MethodsWe conducted simulations to evaluate the power of co-primary and multiple primary endpoints, composite endpoints, and prioritized outcome approaches based on generalized pairwise comparisons with varying correlations, marginal proportions, treatment effects, and number of outcomes. We then conducted a case study on sample size using a clinical trial of a migraine treatment as an example.ResultsThe correlations significantly affected the statistical power and sample size of composite endpoints. The power and sample size of co-primary endpoints remained relatively stable across different correlations, though their power declined substantially when treatment effects were opposite on some components or more than two components were present. While the correlations influenced the power and sample size of all methods assessed, their direction and degree of influence varied between methods. Notably, the method with the greatest power and smallest sample size also differed depending on the correlations. When the correlations were the same between arms, prioritized outcome approaches usually had higher power and smaller sample sizes than other methods.ConclusionsAnticipated correlations and their uncertainty should be considered when selecting statistical methods. Overall, co-primary endpoints remain a reliable option for evaluating the superiority of all components, although they are unsuitable for assessing the balance between treatment effects pointing in different directions. Generalized pairwise comparisons offer a useful alternative to deal with multiple prioritized outcomes, often providing the smallest sample sizes when the correlation structures are shared between the arms.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"301-311"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920978","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":"Exclusion of people from oncology clinical trials based on functional status.","authors":"Nicole D Agaronnik, Mary Linton B Peters, Lisa I Iezzoni","doi":"10.1177/17407745241304114","DOIUrl":"10.1177/17407745241304114","url":null,"abstract":"<p><strong>Background/aims: </strong>People with disability have higher rates of cancer, excluding skin cancer, compared with people without disability. Food and Drug Administration draft guidelines from 2024 address use of performance status criteria to determine eligibility for clinical trials, advocating for less restrictive thresholds. We examined the exclusion of people with disability from clinical trials based on performance status and other criteria.</p><p><strong>Methods: </strong>We reviewed eligibility criteria in approved interventional Phase III and Phase IV oncology clinical trials listed on ClinicalTrails.gov between 1 January 2019 and 31 December 2023. Functional status thresholds were assessed using the Eastern Cooperative Oncology Group Performance Status Scale and Karnofsky Performance Scale in clinical trial eligibility criteria. Qualitative analysis was used to review eligibility criteria relating to functional impairments or disability.</p><p><strong>Results: </strong>Among 96 oncology clinical trials, approximately 40% had restrictive Eastern Cooperative Oncology Group and Karnofsky Performance Scale thresholds, explicitly including only patients with Eastern Cooperative Oncology Group 0 or 1, or equivalent Karnofsky Performance Scale 70 or greater. Only 20% of studies included patients with Eastern Cooperative Oncology Group 2 and Karnofsky Performance Scale 60. Multiple studies contained miscellaneous eligibility criteria that could potentially exclude people with disability. No studies described making accommodations for people with disability to participate in the clinical trial.</p><p><strong>Conclusion: </strong>Draft Food and Drug Administration guidelines recommend including patients with Eastern Cooperative Oncology Group scores of 2 and Karnofsky Performance Scale scores of 60 in oncology clinical trials. We found that oncology clinical trials often exclude people with more restrictive performance status scores than the draft Food and Drug Administration guidelines, as well as other criteria that relate to disability. These estimates provide baseline information for assessing how the 2024 Food and Drug Administration guidance, if finalized, might affect the inclusion of people with disability in future trials.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"367-373"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913834","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":"Challenges in estimating the counterfactual placebo HIV incidence rate from a registration cohort: The PrEPVacc trial.","authors":"Sheila Kansiime, Christian Holm Hansen, Eugene Ruzagira, Sheena McCormack, Richard Hayes, David Dunn","doi":"10.1177/17407745241304721","DOIUrl":"10.1177/17407745241304721","url":null,"abstract":"&lt;p&gt;&lt;p&gt;BackgroundThere is increasing recognition that the interpretation of active-controlled HIV prevention trials should consider the counterfactual placebo HIV incidence rate, that is, the rate that would have been observed if the trial had included a placebo control arm. The PrEPVacc HIV vaccine and pre-exposure prophylaxis trial (NCT04066881) incorporated a pre-trial registration cohort partly for this purpose. In this article, we describe our attempts to model the counterfactual placebo HIV incidence rate from the registration cohort.MethodsPrEPVacc was conducted at four study sites in three African countries. During the set up of the trial, potential participants were invited to join a registration cohort, which included HIV testing every 3 months. The trial included a non-inferiority comparison of two daily, oral pre-exposure prophylaxis regimens (emtricitabine/tenofovir disoproxil fumarate, emtricitabine/tenofovir alafenamide fumarate), administered for a target duration of 26 weeks (until 2 weeks after the third of four vaccinations). We developed a multi-variable Poisson regression model to estimate associations in the registration cohort between HIV incidence and baseline predictors (socio-demographic and behavioural variables) and time-dependent predictors (calendar time, time in follow-up). We then used the estimated regression coefficients together with participant characteristics in the active-controlled pre-exposure prophylaxis trial to predict a counterfactual placebo incidence rate. Sensitivity analyses regarding the effect of calendar period were conducted.ResultsA total of 3255 participants were followed up in the registration cohort between July 2018 and October 2022, and 1512 participants were enrolled in the trial between December 2020 and March 2023. In the registration cohort, 106 participants were diagnosed with HIV over 3638 person-years of follow-up (incidence rate = 2.9/100 person-years of follow-up (95% confidence interval: 2.4-3.5)). The final statistical model included terms for study site, gender, age, occupation, sex after using recreational drugs, time in follow-up, and calendar period. The estimated effect of calendar period was very strong, an overall 37% (95% confidence interval: 19-51) decline per year in adjusted analyses, with evidence that this effect varied by study site. In sensitivity analyses investigating different assumptions about the precise effect of calendar period, the predicted counterfactual placebo incidence ranged between 1.2 and 3.7/100 person-years of follow-up.ConclusionIn principle, the use of a registration cohort is one of the most straightforward and reliable methods for estimating the counterfactual placebo HIV incidence. However, the predictions in PrEPVacc are complicated by an implausibly large calendar time effect, with uncertainty as to whether this can be validly extrapolated over the period of trial follow-up. Other limitations are discussed, along with suggestions for mitiga","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"289-300"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028103","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":"Pivotal trial characteristics and types of endpoints used to support Food and Drug Administration rare disease drug approvals between 2013 and 2022.","authors":"Kyungwan Hong, Bridget Nugent, Abbas Bandukwala, Robert Schuck, York Tomita, Salvatore Pepe, Mary Doi, Scott Winiecki, Kerry Jo Lee","doi":"10.1177/17407745241309318","DOIUrl":"10.1177/17407745241309318","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Background/aimsRare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.S. Food and Drug Administration's Center for Drug Evaluation and Research over the past decade and characterized key regulatory and trial design elements with a focus on the primary efficacy endpoint utilized as the basis of approval.MethodsUsing the Food and Drug Administration's Data Analysis Search Host database, we identified novel new drug applications and biologics license applications with orphan drug designation that were approved between 2013 and 2022 for non-oncologic indications. From Food and Drug Administration review documents and other external databases, we examined characteristics of pivotal trials for the included drugs, such as therapeutic area, trial design, and type of primary efficacy endpoints. Differences in trial design elements associated with primary efficacy endpoint type were assessed such as randomization and blinding. Then, we summarized the primary efficacy endpoint types utilized in pivotal trials by therapeutic area, approval pathway, and whether the disease etiology is well defined.ResultsOne hundred and seven drugs that met our inclusion criteria were approved between 2013 and 2022. Assessment of the 107 drug development programs identified 150 pivotal trials that were subsequently analyzed. The pivotal trials were mostly randomized (80%) and blinded (69.3%). Biomarkers (41.1%) and clinical outcomes (42.1%) were commonly utilized as primary efficacy endpoints. Analysis of the use of clinical trial design elements across trials that utilized biomarkers, clinical outcomes, or composite endpoints did not reveal statistically significant differences. The choice of primary efficacy endpoint varied by the drug's therapeutic area, approval pathway, and whether the indicated disease etiology was well defined. For example, biomarkers were commonly selected as primary efficacy endpoints in hematology drug approvals (70.6%), whereas clinical outcomes were commonly selected in neurology drug approvals (69.6%). Further, if the disease etiology was well defined, biomarkers were more commonly used as primary efficacy endpoints in pivotal trials (44.7%) than if the disease etiology was not well defined (27.3%).DiscussionIn the past 10 years, numerous novel drugs have been approved to treat non-oncologic rare diseases in various therapeutic areas. To demonstrate their efficacy for regulatory approval, biomarkers and clinical outcomes were commonly utilized as primary efficacy endpoints. Biomarkers were not only frequently used as s","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"352-360"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143036919","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}