Clinical Trials最新文献

{"title":"Adaptive trial design and interim decision-making using incomplete longitudinal measurements: Methods and application to myasthenia gravis.","authors":"Kush Kapur, Fien Gistelinck, An Vandebosch, Kelly Van Lancker","doi":"10.1177/17407745261438128","DOIUrl":"https://doi.org/10.1177/17407745261438128","url":null,"abstract":"<p><p>Sample size re-estimation designs using a promising zone framework are widely used adaptive trial methodologies that guide study continuation or modification during interim analyses. Conventional implementations often base interim calculations solely on participants with available primary endpoints, overlooking predictive information from baseline and earlier visits. This underutilization can lead to inefficient interim decision-making. In this work, we adapt semi-parametric efficient estimators that leverage baseline and intermediate data for use within a promising zone sample size re-estimation design. By incorporating information from participants who have not yet reached their primary endpoint, these estimators enable more precise interim estimators while maintaining strict Type I error control through the inverse normal combination function. Using data from the ADAPT study in generalized myasthenia gravis, we illustrate how these methods integrate into a promising zone sample size re-estimation framework. Simulations based on longitudinal profiles of anti-acetylcholine receptor antibody-seronegative participants demonstrate improved operating characteristics compared with the conventional approach, including increased overall power, especially for moderate effect sizes, without inflating the one-sided Type I error. Our findings highlight the practical benefit of applying existing semi-parametric estimators within promising zone sample size re-estimation designs, enabling more efficient and timely interim decision-making in settings with partially observed longitudinal data.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261438128"},"PeriodicalIF":2.2,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856030","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":"Covariate adjustment in randomized clinical trials: From general theory to practical insights.","authors":"Marlena S Bannick, Yanyao Yi, Ting Ye","doi":"10.1177/17407745261442586","DOIUrl":"https://doi.org/10.1177/17407745261442586","url":null,"abstract":"<p><p>Covariate adjustment uses baseline prognostic variables to improve the precision of treatment effect estimates. Recent Food and Drug Administration guidance and scientific consensus emphasize three principles for its use, namely estimand-focused analyses, assumption-lean robustness, and fit-for-purpose variance estimation. Despite substantial methodological progress, practical guidance for trial practitioners remains fragmented. We review covariate adjustment strategies for continuous, discrete, and time-to-event endpoints in randomized trials that adhere to these three principles. We show how unadjusted estimators, as well as linear and non-linear adjusted estimators, can be viewed as special cases of the general augmented inverse probability weighting framework. For time-to-event endpoints, we describe how covariate adjustment can be applied to Kaplan-Meier estimators, log-rank tests, and estimation of the unconditional hazard ratio without altering the estimand or introducing additional assumptions. We also synthesize recent developments in multi-arm trials, covariate-adaptive randomization, data-adaptive covariate selection, and covariate adjustment in interim analyses, and we provide practical insights for implementation. Covariate-adjusted estimators target the same marginal estimands as unadjusted analyses but typically achieve greater efficiency. Linear adjustment with Analysis of Heterogeneous Covariance guarantees asymptotic efficiency gains under minimal assumptions. Augmented inverse probability weighting generalizes covariate adjustment to flexible modeling frameworks and remains consistent even under model misspecification. For survival analysis, covariate-adjusted versions of the log-rank test and Cox model improve power without altering the estimand or requiring additional assumptions. Properly accounting for covariate-adaptive randomization is essential for valid inference. The reviewed methods are implemented in the RobinCar family of R packages: RobinCar and RobinCar2. Covariate adjustment is a principled and practical approach for improving trial efficiency, aligned with current regulatory guidance. By adhering to the principles of estimand-focus, assumption-lean robustness, and fit-for-purpose variance estimation, practitioners can apply covariate adjustment with confidence across diverse trial settings. Further work on evaluating finite-sample performance and re-analyses of completed trials will deepen understanding of covariate adjustment in practice.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261442586"},"PeriodicalIF":2.2,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856088","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":"Estimating clinical trial hazard functions.","authors":"Daniel F Heitjan","doi":"10.1177/17407745261439661","DOIUrl":"https://doi.org/10.1177/17407745261439661","url":null,"abstract":"<p><strong>Background: </strong>Although the analysis of event-based clinical trials commonly relies on assumptions about the underlying hazard functions, in practice it is rare to see estimates of those functions.</p><p><strong>Methods: </strong>I describe conventional and novel methods for estimating the hazard function using discrete and discretized continuous survival models. The conventional approach involves parametric modeling; the novel approach applies Bayesian model averaging to flexible modeling by splines or fractional polynomials. I evaluate the methods in a Monte Carlo study and illustrate them in the analysis of three historical clinical trials.</p><p><strong>Results: </strong>Although flexible models can capture features of the hazard functions-such as multimodality-that parametric models miss, they are not foolproof. Spline modeling was generally the most reliable, in the sense of yielding good coverage probabilities for the mean and median with modest loss of efficiency. In the examples, the discreteness of the measurements-days, weeks, or months-had little effect on the shape of estimated hazard functions. All three data sets showed some evidence of departure from the proportional hazards assumption, but in only one did a test for proportionality detect this departure.</p><p><strong>Conclusion: </strong>Flexible parametric models, estimated in the Bayesian model averaging framework, offer a robust approach to recovering the shape of the hazard function. Analyses of three clinical trial databases suggest that visualization of the hazard function can be a valuable adjunct to conventional survival analysis.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261439661"},"PeriodicalIF":2.2,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764920","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":"Surrogate markers used as efficacy endpoints in NIH-sponsored clinical trials.","authors":"Ayman Mohammad, Samuel Yoon, Joshua D Wallach, Reshma Ramachandran, Joseph S Ross","doi":"10.1177/17407745261437294","DOIUrl":"https://doi.org/10.1177/17407745261437294","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261437294"},"PeriodicalIF":2.2,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697901","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":"Covariate adjustment in randomized trials: An overview.","authors":"Stuart J Pocock","doi":"10.1177/17407745261434564","DOIUrl":"https://doi.org/10.1177/17407745261434564","url":null,"abstract":"<p><p>This article aims to provide a practical overview of the various methods of covariate adjustment in randomized clinical trials leading to recommendation for future practice. Topics covered are baseline adjustment for a quantitative outcome (analysis of covariance), consequences of covariate adjustment for different types of outcome (quantitative, binary, time-to-event), surveys of covariate adjustment as done in published trials, regulatory guidance on covariate adjustment, how big is the gain in statistical power (a simulation study), some pertinent examples in cardiovascular trials, center-adjusted analyses (are they worthwhile?), a brief mention of some alternative approaches to covariate adjustment, other uses of covariates (eg risk models). We conclude that modest gains in statistical power are achieved by adjustment for covariates that influence prognosis.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261434564"},"PeriodicalIF":2.2,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147688427","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":"Evidence supporting European Medicines Agency drug approvals (2020-2023): A cross-sectional study of trial design and outcomes.","authors":"Maximilian Siebert, Laura Caquelin, Florian Naudet, Joseph S Ross, Reshma Ramachandran","doi":"10.1177/17407745261437363","DOIUrl":"https://doi.org/10.1177/17407745261437363","url":null,"abstract":"<p><strong>Background: </strong>The strength and transparency of clinical trial evidence supporting drug approvals have become increasingly scrutinized, particularly considering the increased use of regulatory flexibility and expedited pathways. While US Food and Drug Administration standards have been extensively analyzed, evidence standards at the European Medicines Agency remain less well-characterized. Thus, this study aims to systematically assess the design, quality, and outcomes of pivotal efficacy trials supporting European Medicines Agency drug approvals for new active substances between 2020 and 2023.</p><p><strong>Methods: </strong>We conducted a cross-sectional analysis of drugs receiving positive opinions from the European Medicines Agency's Committee for Medicinal Products for Human Use and subsequent approval by the European Commission between January 2020 and December 2023. Data were extracted from European Public Assessment Reports and European Medicines Agency medicine databases. Key variables included trial design features, primary endpoint type and achievement status, and justification for approval in cases of failed efficacy endpoints.</p><p><strong>Results: </strong>Between 2020 and 2023, 227 new drugs were approved by the European Medicines Agency for 234 indications. Of these, 69 (30.4%) were granted orphan designation, and 53 (23.3%) were approved via an expedited pathway, most commonly conditional approval (29 drugs, 12.8%). Cancer was the leading therapeutic area, accounting for 59 approvals (26.0%). Approvals were supported by 404 pivotal clinical trials. Of the 227 drugs, 177 (78.0%) were supported by at least one randomized controlled trial, of which 138 (78.0%) were blinded. Out of those, 139 (78.5%) were supported exclusively by superiority trials. Regarding endpoint classification, 124/227 drugs (54.6%) relied exclusively on surrogate endpoints and 69 (30.4%) on clinical endpoints. Overall, 22 approvals (9.7%) were supported by at least one pivotal trial in which at least one primary endpoint was not met; in five of these cases (22.7%), the failed trial was the sole pivotal trial. The most common rationale for approval despite null primary results was reliance on the totality of evidence, secondary endpoints, or clinical judgment (nine products; 40.9%).</p><p><strong>Conclusion: </strong>Our findings reveal substantial variability in the design and evidentiary strength of pivotal trials supporting European Medicines Agency approvals between 2020 and 2023. While the majority of studies were randomized controlled trials, reliance on surrogate endpoints was common. That 10% of approvals were based on pivotal trials with null primary endpoints highlights the nuanced role of regulatory judgment in therapeutic evaluation. These findings prompt reflection on evolving evidence standards in drug regulation and underscore the need for transparency and consistent justifications.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261437363"},"PeriodicalIF":2.2,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147627376","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":"Bayesian analysis in confirmatory clinical trials: A narrative review and discussion of current practice.","authors":"Rebecca M Turner, Conor D Tweed, Trinh Duong, Deborah Ford, Michelle N Clements, Mahesh Kb Parmar, Anna Turkova, Ian R White","doi":"10.1177/17407745261437669","DOIUrl":"https://doi.org/10.1177/17407745261437669","url":null,"abstract":"<p><strong>Background: </strong>Bayesian methods allow trial investigators to combine evidence obtained within a clinical trial with relevant evidence that is available outside the trial. Bayesian analyses are now widely used in the drug development process, to inform internal 'go/no-go' decisions about planned studies, for example when deciding whether a drug should proceed from phase II to a phase III trial. However, Bayesian analyses are not commonly used for analysis of phase III (confirmatory) trials.</p><p><strong>Methods: </strong>In this article, we performed a narrative review of confirmatory trials using Bayesian methods for their primary analysis, to explore which types of trials chose Bayesian methods, why they chose a Bayesian analysis and how the methods were used. We reviewed published papers over a 6-year period and explored the characteristics of trials using Bayesian methods for their primary analysis, their reasons for choosing a Bayesian analysis, whether any informative priors were used and if so how they were informed. Next, we selected four trials from the review as case studies and presented their motivation for using Bayesian methods and their Bayesian analyses in more detail.</p><p><strong>Results: </strong>Our narrative review found that the number of Bayesian methods in confirmatory clinical trials has approximately doubled over the past 6 years, reflecting growing familiarity among investigators. Ninety-four papers were eligible for inclusion, presenting results from 69 separate trials. The most common reason given for choosing Bayesian methods was to make direct probability statements about the superiority and/or futility of the interventions evaluated; this was mentioned for 49% of trials. Flexibility in adapting the design or use of Bayesian stopping rules was another very common motivation, cited for 47% of trials. Borrowing information through informative priors was cited for a much smaller proportion (16%) of trials. The majority of trials (75%) specified vague or weakly informative priors for all parameters.</p><p><strong>Conclusion: </strong>Among the reasons given for choosing Bayesian methods, we consider the use of informative priors or making direct probability statements to be the strongest motivations for a Bayesian analysis, because there are no equivalent frequentist approaches. Making direct probability statements was the most common motivation provided, while informative priors were not often used. In settings with recruitment difficulties, we recommend considering borrowing relevant information, to gain power and precision. In all confirmatory trial settings, we recommend that Bayesian approaches are used only with careful justification, investigators make clear whether the methods and priors were pre-planned, and alternative frequentist approaches are considered.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261437669"},"PeriodicalIF":2.2,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147632198","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":"The evolving role of placebo in non-inferiority trials: A scoping review.","authors":"Spencer Cho, Shaheer Nadeem, Valentina Ly, Iman Alhafez, Tim Ramsay, Pil Joo","doi":"10.1177/17407745261430850","DOIUrl":"https://doi.org/10.1177/17407745261430850","url":null,"abstract":"<p><strong>Background: </strong>Non-inferiority trials can be used for efficacy endpoints or safety endpoints, with indirect and direct comparison to placebo. A less common application of this design involves using placebo as the proposed intervention to challenge standard practices that lack evidence of efficacy. However, the methodology of using a placebo in a non-inferiority trial is poorly described in the literature. We performed a scoping review to map how placebo is utilized in randomized controlled trials with a non-inferiority design, with particular attention to studies positioning placebo as a proposed alternative to existing, but unproven, interventions.</p><p><strong>Methods: </strong>We conducted a scoping review of randomized controlled trials using non-inferiority designs with a placebo arm, searching six databases without date or language restrictions. Eligible studies were primary randomized controlled trials with at least one placebo arm evaluated under a non-inferiority hypothesis. Data extraction focused on study characteristics, design elements, rationale for non-inferiority design and margin, and analytical practices.</p><p><strong>Results: </strong>Of 6897 studies screened, 94 met inclusion criteria. Three primary study types were identified: safety (63%), deprescription (20%), and shorter-course (13%) trials. There has been increased use of deprescription and shorter-course studies since 2017. One-third (35%) hypothesized that placebo was non-inferior to active treatment, predominantly in deprescription and shorter-course trials focused on antibiotic use. Most studies (94%) applied non-inferiority analysis to primary outcomes, yet only 22% provided a rationale for non-inferiority design, and despite 96% prespecifying non-inferiority margin, only 41% justified the margin. While 71% used intention-to-treat analysis, only 53% conducted per-protocol analysis. Graphical representation of non-inferiority margins and confidence intervals was reported in 23% of studies.</p><p><strong>Conclusion: </strong>Placebo is increasingly used in non-inferiority trials aimed at evaluating the necessity of standard interventions, including safety, deprescription, and shorter-course designs. However, many trials lack critical methodological transparency. Future studies should clearly justify non-inferiority designs and margins, use both intention-to-treat and per-protocol analyses, and adhere to the Consolidated Standards of Reporting Trials reporting guidelines to enhance interpretability and rigor.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261430850"},"PeriodicalIF":2.2,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147621770","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":"Proceedings of the University of Pennsylvania 17th annual conference on statistical issues in clinical trials: Covariate adjustment in randomized clinical trials: new methods and applications.","authors":"Mary E Putt","doi":"10.1177/17407745261434940","DOIUrl":"https://doi.org/10.1177/17407745261434940","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745261434940"},"PeriodicalIF":2.2,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147608276","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":"Implementing a suicide risk management protocol as part of a multisite clinical trial: Findings and lessons learned.","authors":"Erin Chase, Nicole Moreira, Brittany E Blanchard, Julien Rouvere, Lori Ferro, Jared M Bechtel, Danna L Moore, Daniel Vakoch, Keyne C Law, Jürgen Unützer, John C Fortney","doi":"10.1177/17407745251389222","DOIUrl":"10.1177/17407745251389222","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Although people with mental health disorders are more likely to die by suicide, individuals experiencing suicidality are frequently excluded from clinical trials of mental health treatment due to safety and liability concerns. This approach limits the generalizability of trial results and opportunities for intervention. This descriptive study aimed to report outcomes and lessons learned for a suicide risk management protocol implemented for participants reporting suicidal ideation in a comparative effectiveness clinical trial that enrolled patients screening positive for posttraumatic stress disorder or bipolar disorder. Specifically, we examined the proportion of trial participants reporting suicidal ideation, their chosen risk management plan, suicide attempts, and death by suicide. Also, because few studies have examined whether the survey modality of suicide screening impacts endorsement rates, we compared suicide ideation endorsement, patient demographics, and chosen risk management plans across phone and web survey modalities.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Descriptive statistics were used to report the proportion of participants in the comparative effectiveness trial who reported suicidal ideation and activated the suicide risk management protocol, as well as the chosen risk management plans for those with active suicidal ideation. Chi-square tests of independence and Fisher's exact tests were used to test for differences in demographics, screening question responses, and chosen risk management plans, respectively, between web versus phone survey modalities among those that activated the suicide risk management protocol.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 1004 participants in the trial, 72% endorsed current suicidal ideation or previous suicidal behavior at baseline and activated the study's suicide risk management protocol. There were two suicide attempts in the sample (0.28%), and one of which resulted in death (0.14%). There were no statistically significant differences in SRMP activation between phone and web-based survey modalities. Among participants who activated the suicide risk management protocol and endorsed active suicidal ideation, selection of risk management plans did not vary by survey modality. Participants most frequently opted to visit their community health center (42%) or to call the National Suicide Prevention Lifeline (32%) as their chosen risk management plan.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;We developed and implemented the suicide risk management protocol for a multisite clinical trial enrolling patients with complex mental health conditions. Although a higher proportion of participants activated the SRMP compared to previous trials, rates of suicide attempts and suicide deaths were low. Our findings indicated no differences in positive screening rates among trial participants and no differences in safety plan selection by survey modality among participants entering the SRM","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"133-144"},"PeriodicalIF":2.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12695019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145707697","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}