Clinical Trials最新文献

{"title":"Commentary on \"Shaping the future of clinical trials through strategic foresight\".","authors":"Jitendra Ganju","doi":"10.1177/17407745251414679","DOIUrl":"10.1177/17407745251414679","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"320-322"},"PeriodicalIF":2.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288857","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":"Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning.","authors":"Laura B Balzer, Mark J van der Laan, Maya L Petersen","doi":"10.1177/17407745261417227","DOIUrl":"10.1177/17407745261417227","url":null,"abstract":"<p><p>Covariate adjustment is an approach to improve the precision of trial analyses by adjusting for baseline variables that are prognostic of the primary endpoint. Motivated by the SEARCH Universal HIV Test-and-Treat Trial (2013-2017), we tell our story of developing, evaluating, and implementing a machine learning-based approach for covariate adjustment. We provide the rationale for as well as the practical concerns with such an approach for estimating marginal effects. Using schematics, we illustrate our procedure: targeted machine learning estimation (TMLE) with Adaptive Pre-specification. Briefly, sample-splitting is used to data-adaptively select the combination of estimators of the outcome regression (i.e. the conditional expectation of the outcome given the trial arm and covariates) and known propensity score (i.e. the conditional probability of being randomized to the intervention given the covariates) that minimizes the cross-validated variance estimate and, thereby, maximizes empirical efficiency. We discuss our approach for evaluating finite sample performance with parametric and plasmode simulations, pre-specifying the Statistical Analysis Plan, and unblinding in real-time on video conference with our colleagues from around the world. We present the results from applying our approach in the primary, pre-specified analysis of eight recently published trials (2022-2024). We conclude with practical recommendations and an invitation to implement our approach in the primary analysis of your next trial.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"289-296"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147316691","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":"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":"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":"248-258"},"PeriodicalIF":2.2,"publicationDate":"2026-06-01","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":"Stakeholder views about the responsibilities of principal investigators in multicenter randomized controlled trials.","authors":"Steven Joffe, Elizabeth F Bair, Katharine A Gleason, Deborah E Sellers, Sarah A McGraw, Cary P Gross, Donna T Chen, Eric G Campbell, Michelle M Mello","doi":"10.1177/17407745261417337","DOIUrl":"10.1177/17407745261417337","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;Clinical trials are commonly believed to benefit from the involvement of an academic principal investigator who accepts responsibility for design, conduct, and reporting. Little evidence exists, however, about the importance that diverse stakeholders assign to the principal investigator's role in leadership of trials. Furthermore, few studies have examined whether and how beliefs about the role of the principal investigator might vary by funding source.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted parallel Delphi panel surveys with seven stakeholder groups (principal investigators, patient advocates, journal editors, public funders, industry representatives, United States Food and Drug Administration officials, and clinical trial cooperative-group chairs) to assess the extent to which respondents believed leadership of a multicenter randomized controlled trial by an academic principal investigator to be important, considering publicly and industry-funded trials separately. We then surveyed an international sample of principal investigators (N = 92) who had recently published a multicenter randomized controlled trial in a high-impact general medical, oncology, cardiovascular, or psychiatry journal to assess their normative views on the importance of the academic principal investigator in leading both publicly and industry-funded trials.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Several patterns emerged from the Delphi panel surveys. First, panelists viewed involvement of an identified academic principal investigator as most important at the design and planning and the interpretation and dissemination phases of a trial, as compared with the implementation and data collection phase. Second, panelists generally viewed involvement of an identified academic principal investigator as more important in publicly funded than in industry-funded trials. Finally, panelists representing industry stakeholders and United States Food and Drug Administration officials viewed involvement of an identified academic principal investigator as less important, especially for industry-funded trials, than did other groups. Respondents to the normative principal investigator survey generally endorsed the importance of academic principal investigators in leading multicenter randomized controlled trials, both overall (median rating 6 on the 0-6 point scale) and for trial-specific tasks. Both overall and with respect to specific tasks, however, respondents viewed an academic principal investigator's leadership as more important when considering publicly funded as compared with industry-funded trials.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Although members of most stakeholder groups participating in Delphi surveys view involvement of an academic principal investigator with overall responsibility for a multicenter randomized controlled trial as very important, there are notable differences depending on the respondent's perspective, the specific trial-relat","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"325-335"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12944536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275788","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":"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":"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":"272-281"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13241244/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian design and analysis of two-arm cluster randomised trials using assurance: Extension to binary outcomes and comparison of Markov chain Monte Carlo and Integrated Nested Laplace Approximations.","authors":"Abdullah Aloufi, Kevin J Wilson, Nina Wilson, Lisa Shaw, Christopher Price","doi":"10.1177/17407745261421842","DOIUrl":"10.1177/17407745261421842","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;Bayesian designs for clinical trials using assurance to choose the sample size have been proposed in various trial contexts. Assurance allows for the incorporation of uncertainty on both the treatment effect and nuisance parameters into the sample size calculation. In the case of two-arm cluster randomised trials with continuous outcomes, assurance has been proposed with both a frequentist analysis (hybrid designs) and a Bayesian analysis (fully Bayesian designs). A Bayesian analysis in this context ensures a consistent treatment of probability throughout the design and analysis of the trial. In the fully Bayesian design, inference has been achieved via Markov chain Monte Carlo sampling, and since assurance itself is evaluated via simulation, the result is a computationally intensive and often slow-to-run approach. In the case of two-arm cluster randomised trials with binary outcomes, assurance has not yet been explored to specify sample sizes, either in the hybrid or fully Bayesian case.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This article considers fully Bayesian designs for two-arm cluster randomised trials with continuous and binary outcomes. For the analysis of the trial, we use a (generalised) linear mixed-effects model. We summarise the inference for the treatment effect based on quantiles of the posterior distribution. We use assurance to choose the sample size. In the continuous case, we investigate Integrated Nested Laplace Approximations for inference to speed up calculation of the assurance and compare Integrated Nested Laplace Approximations in computation time and accuracy to Markov chain Monte Carlo. In the binary case, we develop the first fully Bayesian design for cluster randomised trials and conduct a similar comparison between Integrated Nested Laplace Approximations and Markov chain Monte Carlo. We demonstrate our novel approach using assurance to choose sample sizes for the SPEEDY cluster randomised trial, based on the results of a formal prior elicitation exercise with two clinical experts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We report comparisons of Integrated Nested Laplace Approximations and Markov chain Monte Carlo for a range of different scenarios for cluster randomised controlled trials (RCTs), to determine when each inference scheme should be used, balancing the computational cost in terms of speed and accuracy. Overall Markov chain Monte Carlo with a very large number of samples produces very accurate inference but does not scale well in terms of computational speed compared to Integrated Nested Laplace Approximations. Based on our simulation study, we recommend that Integrated Nested Laplace Approximations is used for inference in cluster trials with binary outcomes and large (&lt;i&gt;n&lt;/i&gt;&gt; 500) cluster trials with continuous outcomes, and that Markov chain Monte Carlo is used in smaller (&lt;i&gt;n&lt;/i&gt;≤500) cluster trials with continuous outcomes. Our case study demonstrated how to incorporate the ","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"336-346"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147347342","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":"Confidence interval estimation for the win probability in cluster randomized trials with hierarchical composite endpoints using win fractions.","authors":"Emma Davies Smith, Yun-Hee Choi, Vipul Jairath, Guangyong Zou","doi":"10.1177/17407745261417308","DOIUrl":"10.1177/17407745261417308","url":null,"abstract":"<p><p>Background/AimsCluster randomized trials with multiple endpoints feature complex correlation structures. Estimating treatment effects in a meaningful way that respects differences in scale and clinical importance is challenging. Pairwise comparison methods address these challenges by constructing all pairs featuring one treatment and one control participant, then evaluating endpoints in hierarchical order. For cluster randomized trials featuring such a \"hierarchical composite endpoint,\" we develop large-sample confidence interval estimators and hypothesis tests for the nonparametric treatment effect referred to here as the \"win probability.\"MethodsFor each pair of participants (one treated and one control), responses on each endpoint are compared in order of descending clinical importance until it can be determined which participant responded better (\"won\") or all endpoints are exhausted. Dividing the number of wins attributed to the treatment arm by the total number of \"pairwise comparisons\" yields a point estimate of the win probability. The win probability can be transformed into alternative effect measures, including the \"win difference\" and \"win odds.\" A two-stage procedure, or \"win fraction\" approach, is used to obtain variance estimators for the win probability. Each participant's multivariate response is transformed into a univariate \"win fraction,\" which quantifies the proportion of times they won when compared to all participants in the comparison arm. A working linear mixed model is applied to the win fractions to obtain cluster-adjusted point estimates of the win probability and its variance. Inference proceeds by the central limit theorem. Simulation is used to assess the performance of the proposed estimators for a hierarchical composite endpoint comprised of one binary component (more important) and one continuous component (less important) across a range of cluster trial designs. Performance of an empirical bootstrap estimator is also investigated. A case study using data from the REACT cluster trial demonstrates application of the methods, and corresponding SAS and R code is provided.ResultsSimulation suggests that the nominal 95% coverage probability is well maintained and type I error is controlled. Due to the large-sample nature of our method, confidence intervals may be conservative (over coverage) for fewer than 30 clusters. In comparison, the empirical bootstrap estimator is liberal (under coverage) for all numbers of randomized clusters (up to 50).ConclusionOur win fraction method uses a working linear mixed model to obtain confidence intervals and hypothesis tests which respect coverage and type I error. It is faster than the bootstrap, applicable to multiple components on different scales, bypasses specification of complex correlation matrices, permits adjustment, and can be implemented in existing software.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"347-357"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147269734","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":"17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).","authors":"Frank Harrell, Dylan S Small","doi":"10.1177/17407745261442564","DOIUrl":"10.1177/17407745261442564","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"297-308"},"PeriodicalIF":2.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147970880","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":"Reconsidering registration requirements for trials randomising health care providers.","authors":"Karla Hemming, Monica Taljaard, Laura Kudrna, Joanne E McKenzie, Cory E Goldstein","doi":"10.1177/17407745261429958","DOIUrl":"10.1177/17407745261429958","url":null,"abstract":"<p><p>Clinical trial registration plays a vital role in enhancing transparency, reducing reporting bias, and upholding the integrity of the evidence used in clinical and policy decision-making. The International Committee of Medical Journal Editors (ICMJE) has produced a set of recommendations covering trial registration for its member journals, but they are widely adopted by many journals. It recommends that trials are registered prospectively, and that trial registration is clearly reported in all related manuscripts. However, ICMJE's recommendations are ambiguous when it comes to the registration of clinical trials involving healthcare providers (HCPs), indicating that if trials study the effect of an intervention only on HCPs (e.g. provider knowledge or attitudes), then registration is not necessary. In this commentary, we argue that all clinical trials involving HCPs should be registered for three reasons. First, these trials (like any other research) are at an increased risk of reporting bias should they not be registered prospectively. Second, when HCPs are research participants, they are deserving of the same ethical protections as other types of research participants (and this includes trial registration). Finally, not exempting these trials from registrations would bring ICMJE in line with other international guidelines for health research (which do not exempt them). We conclude by advocating for a revision of the ICMJE trial-registration recommendation.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"358-360"},"PeriodicalIF":2.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147462741","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":"Comment on: Shaping the future of clinical trials through strategic foresight.","authors":"Brian L Wiens","doi":"10.1177/17407745251414681","DOIUrl":"10.1177/17407745251414681","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"323-324"},"PeriodicalIF":2.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147289390","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}