Clinical Trials最新文献

{"title":"Efficient designs for three-sequence stepped wedge trials with continuous recruitment.","authors":"Richard Hooper, Olivier Quintin, Jessica Kasza","doi":"10.1177/17407745241251780","DOIUrl":"10.1177/17407745241251780","url":null,"abstract":"<p><strong>Background/aims: </strong>The standard approach to designing stepped wedge trials that recruit participants in a continuous stream is to divide time into periods of equal length. But the choice of design in such cases is infinitely more flexible: each cluster could cross from the control to the intervention at any point on the continuous time-scale. We consider the case of a stepped wedge design with clusters randomised to just three sequences (designs with small numbers of sequences may be preferred for their simplicity and practicality) and investigate the choice of design that minimises the variance of the treatment effect estimator under different assumptions about the intra-cluster correlation.</p><p><strong>Methods: </strong>We make some simplifying assumptions in order to calculate the variance: in particular that we recruit the same number of participants, <math><mrow><mi>m</mi></mrow></math>, from each cluster over the course of the trial, and that participants present at regularly spaced intervals. We consider an intra-cluster correlation that decays exponentially with separation in time between the presentation of two individuals from the same cluster, from a value of <math><mrow><mi>ρ</mi></mrow></math> for two individuals who present at the same time, to a value of <math><mrow><mi>ρ</mi><mi>τ</mi></mrow></math> for individuals presenting at the start and end of the trial recruitment interval. We restrict attention to three-sequence designs with centrosymmetry - the property that if we reverse time and swap the intervention and control conditions then the design looks the same. We obtain an expression for the variance of the treatment effect estimator adjusted for effects of time, using methods for generalised least squares estimation, and we evaluate this expression numerically for different designs, and for different parameter values.</p><p><strong>Results: </strong>There is a two-dimensional space of possible three-sequence, centrosymmetric stepped wedge designs with continuous recruitment. The variance of the treatment effect estimator for given <math><mrow><mi>ρ</mi></mrow></math> and <math><mrow><mi>τ</mi></mrow></math> can be plotted as a contour map over this space. The shape of this variance surface depends on <math><mrow><mi>τ</mi></mrow></math> and on the parameter <math><mrow><mi>m</mi><mi>ρ</mi><mo>/</mo><mo>(</mo><mn>1</mn><mo>-</mo><mi>ρ</mi><mo>)</mo></mrow></math>, but typically indicates a broad, flat region of close-to-optimal designs. The 'standard' design with equally spaced periods and 1:1:1 allocation rarely performs well, however.</p><p><strong>Conclusions: </strong>In many different settings, a relatively simple design can be found (e.g. one based on simple fractions) that offers close-to-optimal efficiency in that setting. There may also be designs that are robustly efficient over a wide range of settings. Contour maps of the kind we illustrate can help guide this choice. If efficiency is offered a","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"723-733"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074965","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":"Society for Clinical Trials Data Monitoring Committee initiative website: Closing the gap.","authors":"David L DeMets, Susan Halabi, Lehana Thabane, Janet Wittes","doi":"10.1177/17407745241238393","DOIUrl":"10.1177/17407745241238393","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"763-764"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140326457","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 comparison of computational algorithms for the Bayesian analysis of clinical trials.","authors":"Ziming Chen, Jeffrey S Berger, Lana A Castellucci, Michael Farkouh, Ewan C Goligher, Erinn M Hade, Beverley J Hunt, Lucy Z Kornblith, Patrick R Lawler, Eric S Leifer, Elizabeth Lorenzi, Matthew D Neal, Ryan Zarychanski, Anna Heath","doi":"10.1177/17407745241247334","DOIUrl":"10.1177/17407745241247334","url":null,"abstract":"<p><strong>Background: </strong>Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design.</p><p><strong>Methods: </strong>Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded.</p><p><strong>Results: </strong>The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS.</p><p><strong>Conclusion: </str","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"689-700"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140944214","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":"Commentary on Astrachan et al. The transmutation of research risk in pragmatic clinical trials.","authors":"Jonathan Kimmelman","doi":"10.1177/17407745241266168","DOIUrl":"10.1177/17407745241266168","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"666-668"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141987543","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":"Comparison of Bayesian and frequentist monitoring boundaries motivated by the Multiplatform Randomized Clinical Trial.","authors":"Jungnam Joo, Eric S Leifer, Michael A Proschan, James F Troendle, Harmony R Reynolds, Erinn A Hade, Patrick R Lawler, Dong-Yun Kim, Nancy L Geller","doi":"10.1177/17407745241244801","DOIUrl":"10.1177/17407745241244801","url":null,"abstract":"<p><strong>Background: </strong>The coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional \"frequentist\" guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary.</p><p><strong>Methods: </strong>The mpRCT's efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT's futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients' data with imaginary patients' data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O'Brien-Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline.</p><p><strong>Results: </strong>A Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O'Brien-Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary.</p><p><strong>Conclusions: </strong>In a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O'Brien-Fleming boundary. This can be accomplished with either Bayesian or frequentist methods.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"701-709"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955509","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":"Individualized clinical decisions within standard-of-care pragmatic clinical trials: Implications for consent.","authors":"Isabel M Astrachan, James Flory, Scott Yh Kim","doi":"10.1177/17407745241266155","DOIUrl":"10.1177/17407745241266155","url":null,"abstract":"<p><p>Pragmatic clinical trials of standard-of-care interventions compare the relative merits of medical treatments already in use. Traditional research informed consent processes pose significant obstacles to these trials, raising the question of whether they may be conducted with alteration or waiver of informed consent. However, to even be eligible, such a trial in the United States must have no more than minimal research risk. We argue that standard-of-care pragmatic clinical trials can be designed to ensure that they are minimal research risk if the random assignment of an intervention in a pragmatic clinical trial can accommodate individualized, clinically motivated decision-making for each participant. Such a design will ensure that the patient-participants are not exposed to any risks beyond the clinical risks of the interventions, and thus, the trial will have minimal research risk. We explain the logic of this view by comparing three scenarios of standard-of-care pragmatic clinical trials: one with informed consent, one without informed consent, and one recently proposed design called Decision Architecture Randomization Trial. We then conclude by briefly showing that our proposal suggests a natural way to determine when to use an alteration versus a waiver of informed consent.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"659-665"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141987544","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":"Challenges in conducting efficacy trials for new COVID-19 vaccines in developed countries.","authors":"Rafael Dal-Ré, Emmanuel Bottieau, Odile Launay, Frits R Rosendaal, Brigitte Schwarzer-Daum","doi":"10.1177/17407745241238925","DOIUrl":"10.1177/17407745241238925","url":null,"abstract":"<p><p>The protection from COVID-19 vaccination wanes a few months post-administration of the primary vaccination series or booster doses. New COVID-19 vaccine candidates aiming to help control COVID-19 should show long-term efficacy, allowing a possible annual administration. Until correlates of protection are strongly associated with long-term protection, it has been suggested that any new COVID-19 vaccine candidate must demonstrate at least 75% efficacy (although a 40%-60% efficacy would be sufficient) at 12 months in preventing illness in all age groups within a large randomized controlled efficacy trial. This article discusses four of the many scientific, ethical, and operational challenges that these trials will face in developed countries, focusing on a pivotal trial in adults. These challenges are (1) the comparator and trial population; (2) how to enroll sufficient numbers of adult participants of all age groups considering that countries will recommend COVID-19 booster doses to different populations; (3) whether having access to a comparator booster for the trial is actually feasible; and (4) the changing epidemiology of severe acute respiratory syndrome coronavirus 2 across countries involved in the trial. It is desirable that regulatory agencies publish guidance on the requirements that a trial like the one discussed should comply with to be acceptable from a regulatory standpoint. Ideally, this should happen even before there is a vaccine candidate that could fulfill the requirements mentioned above, as it would allow an open discussion among all stakeholders on its appropriateness and feasibility.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"754-758"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140317946","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":"Taking clinical decisions seriously in standard-of-care pragmatic clinical trials.","authors":"Isabel M Astrachan, James Flory, Scott Yh Kim","doi":"10.1177/17407745241266152","DOIUrl":"10.1177/17407745241266152","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"669-670"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141987545","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":"Ethical considerations for sharing aggregate results from pragmatic clinical trials.","authors":"Stephanie R Morain, Abigail Brickler, Joseph Ali, Patricia Pearl O'Rourke, Kayte Spector-Bagdady, Benjamin Wilfond, Vasiliki Rahimzadeh, Caleigh Propes, Kayla Mehl, David Wendler","doi":"10.1177/17407745241290782","DOIUrl":"10.1177/17407745241290782","url":null,"abstract":"<p><p>A growing literature has explored the ethical obligations and current practices related to sharing aggregate results with research participants. However, no prior work has examined these issues in the context of pragmatic clinical trials. Several characteristics of pragmatic clinical trials may complicate both the ethics and the logistics of sharing aggregate results. Among these characteristics include that pragmatic clinical trials may affect the rights, welfare, and interests of not only patient-subjects but also clinicians, meaning that results may be owed to a broader range of groups than typically considered in other research contexts. In addition, some pragmatic clinical trials are conducted under a waiver of informed consent, meaning sharing results may alert participants that they were enrolled without their consent. This article explores the ethical dimensions that can inform decision-making about sharing aggregate results from pragmatic clinical trials, and provides recommendations for that sharing. A central insight is that healthcare institutions-as key partners for the conduct of pragmatic clinical trials-must also be key partners in decision-making about sharing aggregate pragmatic clinical trial results. We conclude with insights for future research.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241290782"},"PeriodicalIF":2.2,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715141","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":"Detecting irregularities in randomized controlled trials using machine learning.","authors":"Walter Nelson, Jeremy Petch, Jonathan Ranisau, Robin Zhao, Kumar Balasubramanian, Shrikant I Bangdiwala","doi":"10.1177/17407745241297947","DOIUrl":"https://doi.org/10.1177/17407745241297947","url":null,"abstract":"<p><strong>Background: </strong>Over the course of a clinical trial, irregularities may arise in the data. Trialists implement human-intensive, expensive central statistical monitoring procedures to identify and correct these irregularities before the results of the trial are analyzed and disseminated. Machine learning algorithms have shown promise for identifying center-level irregularities in multi-center clinical trials with minimal human intervention. We aimed to characterize the form-level data irregularities in several historical clinical trials and evaluate the ability of a machine learning-based outlier detection algorithm to identify them.</p><p><strong>Methods: </strong>Data irregularities previously identified by humans in historical clinical trials were ascertained by comparing preliminary snapshots of the trial databases to the final, locked databases. We measured the ability of a machine learning based outlier detection algorithm to identify form-level irregularities using concordance (area under the receiver operator characteristic), positive predictive value (precision), and sensitivity (recall).</p><p><strong>Results: </strong>We examined preliminary snapshots of seven historical clinical trials which randomized a total of 77,001 participants. We extracted a total of 1,267,484 completed entries from 358 case report forms containing irregularities from all snapshots across all trials, containing a total of 24,850 form-wide irregularities (median per-form form-level irregularity rate: 1.81%). Our proposed machine learning algorithm detects form-level irregularities with a median concordance of 0.74 (interquartile range = 0.57-0.89), slightly exceeding the performance of a previously proposed machine learning approach with a median area under the receiver operator characteristic of 0.73 (interquartile range = 0.54-0.88).</p><p><strong>Conclusion: </strong>Data irregularities in historical clinical trials were ascertained by comparing preliminary snapshots of the trial database to the final database. These irregularities can be categorized according to their scope. Irregularities can be successfully detected by a machine learning algorithm as early or earlier than a human can, without human intervention. Such an approach may complement existing techniques for central statistical monitoring in large multi-center randomized controlled trials and possibly improve the efficiency of costly data verification processes.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241297947"},"PeriodicalIF":2.2,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715120","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}