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- Afternoon discussion: Statistical issues in clinical trials conference on
dose finding-
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Authors: Anna Heath; Kelley M Kidwell
Abstract: Clinical Trials, Ahead of Print.
The adoption of innovative, model-based, and computationally intensive clinical trial designs is challenged by barriers including clinician engagement, regulatory acceptance, dissemination beyond major research institutions, and patient accrual. This ...
Citation: Clinical Trials
PubDate: 2025-06-27T11:34:59Z
DOI: 10.1177/17407745251350598
- Approaches to ensure quality of information provision and consent
processes for vaccine clinical trial participation in Sub-Saharan Africa:
A scoping review-
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Authors: Aitana Juan-Giner; Elena Carrillo-Alvarez, Cristina Enguita-Fernàndez
Abstract: Clinical Trials, Ahead of Print.
Background/AimsTo respect the rights and wellbeing of research participants, these should receive information at all stages of the trial, and procedures should be put in place to ensure a valid consent that promotes an informed, autonomous and voluntary ...
Citation: Clinical Trials
PubDate: 2025-06-27T11:31:29Z
DOI: 10.1177/17407745251346134
- Proceedings of the University of Pennsylvania 16th annual conference on
statistical issues in clinical trials: Optimizing dose selection across
the clinical trials spectrum-
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Authors: Mary E Putt; Pamela A Shaw
Abstract: Clinical Trials, Ahead of Print.
Citation: Clinical Trials
PubDate: 2025-06-10T10:14:33Z
DOI: 10.1177/17407745251346836
- Developing a research coordinator workforce: A case study of a hospital
and university collaboration-
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Authors: April M Crawford; Steven L Arxer, James P LePage
Abstract: Clinical Trials, Ahead of Print.
Citation: Clinical Trials
PubDate: 2025-06-10T10:11:46Z
DOI: 10.1177/17407745251338574
- Comparison of adaptive seamless Phase 2/3 designs for dose selection in
clinical trials with multiple endpoints-
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Authors: Man Jin
Abstract: Clinical Trials, Ahead of Print.
Adaptive seamless Phase 2/3 designs provide possible pathways to expedite drug development by combining dose selection and confirmatory evaluation on the selected dose with the control group in the same trial. Various methods have been developed to ...
Citation: Clinical Trials
PubDate: 2025-05-26T07:03:50Z
DOI: 10.1177/17407745251338592
- Precision medicine evaluation of heterogeneity of treatment effect for a
time-to-event outcome with application in a trial of Initial treatment for
people living with HIV-
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Authors: Yu Zheng; Judy S Currier, Michael D Hughes
Abstract: Clinical Trials, Ahead of Print.
BackgroundEvaluation of heterogeneity of treatment effect among participants in large randomized clinical trials may provide insights as to the value of individualizing clinical decisions. The effect modeling approach to predictive heterogeneity of ...
Citation: Clinical Trials
PubDate: 2025-05-22T07:13:43Z
DOI: 10.1177/17407745251338558
- Causal inference in randomized trials with partial clustering
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Authors: Joshua R Nugent; Elijah Kakande, Gabriel Chamie, Jane Kabami, Asiphas Owaraganise, Diane V Havlir, Moses Kamya, Laura B Balzer
Abstract: Clinical Trials, Ahead of Print.
Background:Participant dependence, if present, must be accounted for in the analysis of randomized trials. This dependence, also referred to as “clustering,” can occur in one or more trial arms. This dependence may predate randomization or arise after ...
Citation: Clinical Trials
PubDate: 2025-05-02T07:23:32Z
DOI: 10.1177/17407745251333779
- Identification and mitigation of a systematic analysis error in a
multicenter dual-energy x-ray absorptiometry study-
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Authors: Gayle M Lorenzi; Barbara H Braffett, Ionut Bebu, Victoria R Trapani, Jye-Yu C Backlund, Kaleigh Farrell, Rose A Gubitosi-Klug, Ann V Schwartz,
Abstract: Clinical Trials, Ahead of Print.
Background/AimsData integrity in multicenter and longitudinal studies requires implementation of standardized reproducible methods throughout the data collection, analysis, and reporting process. This requirement is heightened when results are shared with ...
Citation: Clinical Trials
PubDate: 2025-04-22T09:47:58Z
DOI: 10.1177/17407745251328257
- Commentary on Wittes et al: Aspirin for primary prevention of CV events
– Rationally robust' Statistically significant' Clinically convincing'-
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Authors: John GF Cleland; Danyaal Anzar
Abstract: Clinical Trials, Ahead of Print.
Citation: Clinical Trials
PubDate: 2025-04-01T07:10:12Z
DOI: 10.1177/17407745251324865
- Aspirin in primary prevention: Undue reliance on an uninformative trial
led to misinformed clinical guidelines-
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Authors: Janet Wittes; David L DeMets, KyungMann Kim, Dennis G Maki, Marc A Pfeffer, J Michael Gaziano, Panagiota Kitsantas, Charles H Hennekens, Sarah K Wood
Abstract: Clinical Trials, Ahead of Print.
Best practices for design, conduct, analysis, and interpretation of randomized controlled trials should adhere to rigorous statistical principles. The reliable detection of small effects of treatment should be based on results reported from the primary ...
Citation: Clinical Trials
PubDate: 2025-04-01T07:03:20Z
DOI: 10.1177/17407745251324866
- Response to Cleland and Anzar
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Authors: Janet Wittes; David L DeMets, KyungMann Kim, Dennis G Maki, Marc A Pfeffer, J Michael Gaziano, Panagiota Kitsantas, Charles H Hennekens, Sarah K Wood
Abstract: Clinical Trials, Ahead of Print.
Citation: Clinical Trials
PubDate: 2025-04-01T06:59:12Z
DOI: 10.1177/17407745251324843
- Quality management of a multi-center randomized controlled feeding trial:
A prospective observational study-
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Authors: Xiayan Chen; Huijuan Li, Lin Feng, Xi Lan, Shuyi Li, Yanfang Zhao, Guo Zeng, Huilian Zhu, Jianqin Sun, Yanfang Wang, Yangfeng Wu
Abstract: Clinical Trials, Ahead of Print.
BackgroundNutrition and dietary trials are often prone to bias, leading to inaccurate or questionable estimates of intervention efficacy. However, reports on quality management practices of well-controlled dietary trials are scarce. This study aims to ...
Citation: Clinical Trials
PubDate: 2025-03-31T05:29:39Z
DOI: 10.1177/17407745251324653
- Building a professionally recognised clinical trial workforce: Is it time
for an education and accreditation strategy'-
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Authors: Simone Spark; Prudence Perry, Thobekile Mthethwa-Pitt, Dragan Ilic, Anne Woollett, Sophia Zoungas, Marina Skiba
Abstract: Clinical Trials, Ahead of Print.
Evidence-based medicine relies heavily on well-conducted clinical trials. Australia lacks a discipline-specific education pathway to provide the specialist skills necessary to conduct clinical trials to the highest standards. Unlike allied health ...
Citation: Clinical Trials
PubDate: 2025-03-27T11:24:04Z
DOI: 10.1177/17407745251328287
- Design considerations for randomized comparisons of neoadjuvant–adjuvant
versus adjuvant-only cancer immunotherapy when tumor measurement schedules
do not align (SWOG S1801)-
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Authors: Megan Othus; Elad Sharon, Michael C Wu, Vernon K Sondak, Antoni Ribas, Sapna P Patel
Abstract: Clinical Trials, Ahead of Print.
BackgroundIn 2022, SWOG S1801 was the first trial to demonstrate that single-agent anti-PD-1 checkpoint inhibition used as neoadjuvant–adjuvant therapy leads to significantly improved outcomes compared to adjuvant-only therapy. Endpoints in trials ...
Citation: Clinical Trials
PubDate: 2025-03-18T12:49:15Z
DOI: 10.1177/17407745251321371
- Evaluating the use of text-message reminders and personalised text-message
reminders on the return of participant questionnaires in trials, a
systematic review and meta-analysis-
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Authors: Laura Doherty; Catherine Arundel, Elizabeth Coleman, Ailish Byrne, Katherine Jones
Abstract: Clinical Trials, Ahead of Print.
Background:Randomised controlled trials are widely accepted as the gold standard research methodology for the evaluation of interventions. However, they often display poor participant retention. To prevent this, various participant interventions have been ...
Citation: Clinical Trials
PubDate: 2025-03-12T01:08:51Z
DOI: 10.1177/17407745251320888
- Impact of differences between interim and post-interim analysis
populations on outcomes of a group sequential trial: Example of the
MOVe-OUT study-
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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; 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
Abstract: Clinical Trials, Ahead of Print.
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 disproportionately impact prespecified efficacy outcomes at different timepoints. Shifts in prognostic factors may introduce additional variability that can be difficult to disentangle from temporal trends in epidemiology (e.g. evolutionary changes in the causative pathogen) or disease management.(ClinicalTrials.gov: NCT04575597.)
Citation: Clinical Trials
PubDate: 2025-03-03T05:21:34Z
DOI: 10.1177/17407745251313925
- From RAGs to riches: Utilizing large language models to write documents
for clinical trials-
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Authors: Nigel Markey, Ilyass El-Mansouri, Gaetan Rensonnet, Casper van Langen, Christoph Meier; Ilyass El-Mansouri, Gaetan Rensonnet, Casper van Langen, Christoph Meier
Abstract: Clinical Trials, Ahead of Print.
Background/AimsClinical trials require numerous documents to be written: Protocols, consent forms, clinical study reports, and many others. Large language models offer the potential to rapidly generate first-draft versions of these documents; however, there are concerns about the quality of their output. Here, we report an evaluation of how good large language models are at generating sections of one such document, clinical trial protocols.MethodsUsing an off-the-shelf large language model, we generated protocol sections for a broad range of diseases and clinical trial phases. Each of these document sections we assessed across four dimensions: Clinical thinking and logic; Transparency and references; Medical and clinical terminology; and Content relevance and suitability. To improve performance, we used the retrieval-augmented generation method to enhance the large language model with accurate up-to-date information, including regulatory guidance documents and data from ClinicalTrials.gov. Using this retrieval-augmented generation large language model, we regenerated the same protocol sections and assessed them across the same four dimensions.ResultsWe find that the off-the-shelf large language model delivers reasonable results, especially when assessing content relevance and the correct use of medical and clinical terminology, with scores of over 80%. However, the off-the-shelf large language model shows limited performance in clinical thinking and logic and transparency and references, with assessment scores of ≈40% or less. The use of retrieval-augmented generation substantially improves the writing quality of the large language model, with clinical thinking and logic and transparency and references scores increasing to ≈80%. The retrieval-augmented generation method thus greatly improves the practical usability of large language models for clinical trial-related writing.DiscussionOur results suggest that hybrid large language model architectures, such as the retrieval-augmented generation method we utilized, offer strong potential for clinical trial-related writing, including a wide variety of documents. This is potentially transformative, since it addresses several major bottlenecks of drug development.
Citation: Clinical Trials
PubDate: 2025-02-27T02:02:04Z
DOI: 10.1177/17407745251320806
- Hybrid sample size calculations for cluster randomised trials using
assurance-
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Authors: S. Faye Williamson, Svetlana V Tishkovskaya, Kevin J Wilson; Svetlana V Tishkovskaya, Kevin J Wilson
Abstract: Clinical Trials, Ahead of Print.
Background/Aims:Sample size determination for cluster randomised trials is challenging because it requires robust estimation of the intra-cluster correlation coefficient. Typically, the sample size is chosen to provide a certain level of power to reject the null hypothesis in a two-sample hypothesis test. This relies on the minimal clinically important difference and estimates for the overall standard deviation, the intra-cluster correlation coefficient and, if cluster sizes are assumed to be unequal, the coefficient of variation of the cluster size. Varying any of these parameters can have a strong effect on the required sample size. In particular, it is very sensitive to small differences in the intra-cluster correlation coefficient. A relevant intra-cluster correlation coefficient estimate is often not available, or the available estimate is imprecise due to being based on studies with low numbers of clusters. If the intra-cluster correlation coefficient value used in the power calculation is far from the unknown true value, this could lead to trials which are substantially over- or under-powered.Methods:In this article, we propose a hybrid approach using Bayesian assurance to determine the sample size for a cluster randomised trial in combination with a frequentist analysis. Assurance is an alternative to traditional power, which incorporates the uncertainty on key parameters through a prior distribution. We suggest specifying prior distributions for the overall standard deviation, intra-cluster correlation coefficient and coefficient of variation of the cluster size, while still utilising the minimal clinically important difference. We illustrate the approach through the design of a cluster randomised trial in post-stroke incontinence and compare the results to those obtained from a standard power calculation.Results:We show that assurance can be used to calculate a sample size based on an elicited prior distribution for the intra-cluster correlation coefficient, whereas a power calculation discards all of the information in the prior except for a single point estimate. Results show that this approach can avoid misspecifying sample sizes when the prior medians for the intra-cluster correlation coefficient are very similar, but the underlying prior distributions exhibit quite different behaviour. Incorporating uncertainty on all three of the nuisance parameters, rather than only on the intra-cluster correlation coefficient, does not notably increase the required sample size.Conclusion:Assurance provides a better understanding of the probability of success of a trial given a particular minimal clinically important difference and can be used instead of power to produce sample sizes that are more robust to parameter uncertainty. This is especially useful when there is difficulty obtaining reliable parameter estimates.
Citation: Clinical Trials
PubDate: 2025-02-12T05:54:47Z
DOI: 10.1177/17407745241312635
- Characterization of studies considered and required under Medicare’s
coverage with evidence development program-
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Authors: Maryam Mooghali, Osman Moneer, Guneet Janda, Joseph S Ross, Sanket S Dhruva, Reshma Ramachandran; Osman Moneer, Guneet Janda, Joseph S Ross, Sanket S Dhruva, Reshma Ramachandran
Abstract: Clinical Trials, Ahead of Print.
IntroductionIn 2005, the Centers for Medicare and Medicaid Services introduced the Coverage with Evidence Development program for items and services with limited evidence of benefit and harm for Medicare beneficiaries, aiming to generate evidence to determine whether they meet the statutory “reasonable and necessary” criteria for coverage. Coverage with Evidence Development requires participation in clinical studies approved by the Centers for Medicare and Medicaid Services (i.e. Coverage with Evidence Development-approved studies) as a condition of coverage. We examined the quality of evidence generated by Coverage with Evidence Development-approved studies compared with those that informed Centers for Medicare and Medicaid Services’ initial Coverage with Evidence Development decisions (i.e. National Coverage Determination studies).MethodsUsing Centers for Medicare and Medicaid Services’ webpage, we identified all items and services covered under Coverage with Evidence Development and their Coverage with Evidence Development-approved studies. Through searching PubMed and Google Scholar, we identified original research articles that reported results for primary endpoints of Coverage with Evidence Development-approved studies. We then reviewed the initial Coverage with Evidence Development decision memos and identified National Coverage Determination studies that were original research.We characterized and compared Coverage with Evidence Development-approved studies and National Coverage Determination studies.ResultsFrom 2005 to 2023, 26 items and services were covered under the Coverage with Evidence Development program, associated with 196 National Coverage Determination studies (170 (86.7%) clinical trials and 26 (13.3%) registries) and 116 unique Coverage with Evidence Development-approved studies (86 (74.1%) clinical trials, 23 (19.8%) registries, 4 (3.4%) claims-based studies, and 3 (2.6%) expanded access studies). Among clinical trial studies, National Coverage Determination studies and Coverage with Evidence Development-approved studies did not differ with respect to multi-arm design (59.4% vs 68.6%; p = 0.15). However, among multi-arm clinical trial studies, National Coverage Determination studies were less likely than Coverage with Evidence Development-approved studies to be randomized (52.5% vs 93.2%; p
Citation: Clinical Trials
PubDate: 2025-02-08T09:21:45Z
DOI: 10.1177/17407745251313979
- Examining the bias-efficiency tradeoff from incorporation of nonconcurrent
controls in platform trials: A simulation study example from the adaptive
COVID-19 treatment trial-
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Authors: Tyler Bonnett, Gail E Potter, Lori E Dodd; Gail E Potter, Lori E Dodd
Abstract: Clinical Trials, Ahead of Print.
Background:Platform trials typically feature a shared control arm and multiple experimental treatment arms. Staggered entry and exit of arms splits the control group into two cohorts: those randomized during the same period in which the experimental arm was open (concurrent controls) and those randomized outside that period (nonconcurrent controls). Combining these control groups may offer increased statistical power but can lead to bias if analyses do not account for time trends in the response variable. Proposed methods of adjustment for time may increase type I error rates when time trends impact arms unequally or when large, sudden changes to the response rate occur. However, there has been limited exploration of the degree of type I error inflation one can plausibly expect in real-world scenarios.Methods:We use data from the Adaptive COVID-19 Treatment Trial (ACTT) to mimic a realistic platform trial with a remdesivir control arm. We compare four strategies for estimating the effect of interferon beta-1a (the ACTT-3 experimental arm) relative to remdesivir (data from ACTT-1, ACTT-2, and ACTT-3) on recovery and death by day 29: utilizing concurrent controls only (the prespecified analysis), pooling all remdesivir arm data without adjustment (the “unadjusted-pooled” analysis), adjusting for time as a categorical variable, and a Bayesian hierarchical model implementation which adjusts for time trends using smoothing techniques (the “Bayesian time machine”). We compare type I error rates and relative efficiency of each method in simulation settings based on observed ACTT remdesivir arm data.Results:The unadjusted-pooled approach provided substantially different estimates of the effect of interferon beta-1a relative to remdesivir compared with the concurrent-only and model-based approaches, indicating that changes in recovery and death rates over time were not ignorable across different stages of ACTT. The model-based approaches rely on an assumption of constant treatment effects for each arm in the platform relative to control; error rates more than doubled in settings where this was not satisfied. Relative efficiency of the model-based approaches compared with the concurrent-only analysis was moderate.Conclusions:In simulation settings where key model assumptions were not met, potential efficiency gains from incorporation of nonconcurrent controls were outweighed by the risk of substantial type I error rate inflation. This leads us to advise against these strategies for primary analyses in confirmatory clinical trials, aligning with current FDA guidance advising against comparisons to nonconcurrent controls in COVID-19 settings. The model-based adjustment methods may be useful in other settings, but we recommend performing the concurrent-only analysis as a reference for assessing the degree to which nonconcurrent controls drive results.
Citation: Clinical Trials
PubDate: 2025-02-08T09:16:31Z
DOI: 10.1177/17407745251313928
- Pivotal trial characteristics and types of endpoints used to support Food
-
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Authors: Kyungwan Hong, Bridget Nugent, Abbas Bandukwala, Robert Schuck, York Tomita, Salvatore Pepe, Mary Doi, Scott Winiecki, Kerry Jo Lee; Bridget Nugent, Abbas Bandukwala, Robert Schuck, York Tomita, Salvatore Pepe, Mary Doi, Scott Winiecki, Kerry Jo Lee
Abstract: Clinical Trials, Ahead of Print.
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 surrogate efficacy endpoints in accelerated approvals, but also in traditionally approved rare disease drugs. The choice of primary efficacy endpoints varied by therapeutic area, approval pathway, and understanding of disease etiology.
Citation: Clinical Trials
PubDate: 2025-01-25T10:46:59Z
DOI: 10.1177/17407745241309318
- Adaptive promising zone design for sequential parallel comparison design
with continuous outcomes-
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Authors: Xinlin Lu, Guogen Shan; Guogen ShanDepartment of Biostatistics, University of Florida, Gainesville, FL, USA
Abstract: Clinical Trials, Ahead of Print.
Introduction:The sequential parallel comparison design has emerged as a valuable tool in clinical trials with high placebo response rates. To further enhance its efficiency and effectiveness, adaptive strategies, such as sample size adjustment and allocation ratio modification can be employed.Methods:We compared the performance of Jennison and Turnbull’s method and the Promising Zone approach for sample size adjustment in a two-phase sequential parallel comparison design study. We also evaluated the impact of allocation ratio adjustments using Neyman and Optimal allocation strategies. Various scenarios were simulated to assess the effects of different design parameters, including weight in the test statistic, initial randomization ratio, and interim analysis timing.Results:The Promising Zone approach demonstrated superior or comparable power to Jennison and Turnbull’s method at equivalent expected sample sizes while maintaining the intuitive property that more promising interim results lead to smaller required follow-up sample sizes. However, the Promising Zone approach may require a larger maximum possible sample size in some cases. The addition of allocation ratio adjustments offered minimal improvements overall, but showed potential benefits when the variance in the treatment group was larger than that in the placebo group. We also applied our findings to a real-world example from the AVP-923 trial in patients with Alzheimer’s disease-related agitation, demonstrating the practical implications of adaptive sequential parallel comparison designs in clinical research.Discussion:Adaptive strategies can significantly enhance the efficiency of sequential parallel comparison designs. The choice between sample size adjustment methods should consider trade-offs between power, expected sample size, and maximum adjusted sample size. Although allocation ratio adjustments showed limited overall impact, they may be beneficial in specific scenarios. Future research should explore the application of these adaptive strategies to binary and survival outcomes in sequential parallel comparison designs.
Citation: Clinical Trials
PubDate: 2025-01-25T10:20:00Z
DOI: 10.1177/17407745241309056
- Experiences with low-intervention clinical trials—the new category under
the European Union Clinical Trials Regulation-
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Authors: Amos J. de Jong, Helga Gardarsdottir, Yared Santa-Ana-Tellez, Anthonius de Boer, Mira GP Zuidgeest; Helga Gardarsdottir, Yared Santa-Ana-Tellez, Anthonius de Boer, Mira GP Zuidgeest
Abstract: Clinical Trials, Ahead of Print.
Background/AimsLow-intervention clinical trials have been established under the European Union Clinical Trials Regulation (EU 536/2014) which aims to simplify the conduct of clinical trials with authorized medicinal products. There is limited experience with conducting low-intervention trials. Therefore, this study aims to report on experiences and perceived (dis)advantages of low-intervention trials.MethodsWe surveyed representatives of all individual clinical trials registered on the public website of the European Union Clinical Trials Information System between 31 January 2022 and 1 December 2023 that evaluated authorized investigational medicinal products and had at least one investigative site in the European Union. These representatives were approached between June 2023 and January 2024.ResultsWe received 70 responses (response rate 21%). Of the respondents, 31 represented a trial registered as low-intervention trial, and 39 represented a trial not registered as a low-intervention trial (hereafter “regular trials”). Simplified clinical trial monitoring and an easier regulatory approval process were perceived as the main advantages of low-intervention trials, with respectively 44% and 34% of the respondents indicating this to be an advantage in low-intervention trials. However, the respondents experienced that stringent and unclear regulatory requirements impeded the conduct of low-intervention trials. Respondents involved with regular trials indicated that 39% of the regular trials met the criteria of a low-intervention trial but were not registered as such, among others due to unfamiliarity with this trial category.ConclusionsWe argue that the simplified procedures for low-intervention trials should be more detailed—for example in regulatory guidance—in the future to further simplify the conduct of clinical trials with authorized investigational medicinal products.
Citation: Clinical Trials
PubDate: 2025-01-23T06:15:58Z
DOI: 10.1177/17407745241309293
- Military influences on the evolution of clinical trials throughout history
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Authors: Kamil Malshy, Alexis Steinmetz, Kit Yuen, Jathin Bandari, Ronald Rabinowitz; Alexis Steinmetz, Kit Yuen, Jathin Bandari, Ronald Rabinowitz
Abstract: Clinical Trials, Ahead of Print.
Clinical trials of drugs, procedures, and other therapies play a crucial role in advancing medical science by evaluating the safety, efficacy, and optimal use of medical interventions. The design and implementation of these trials have evolved significantly over time, reflecting advancements in medicine, ethics, and methodology. Early historical examples, such as King Nebuchadnezzar II’s and his captives’ dietary experiment and Ambroise Paré’s treatment of gunshot wounds, laid some foundational principles of trial design. The momentum of clinical trial development increased notably with James Lind’s 1747 trial for scurvy and continued to progress during World War I with innovations in blood transfusion techniques. World War II (WWII) marked a pivotal moment with breakthroughs in oncology, including the development of the first modern chemotherapeutic agents derived from mustard gas and the introduction of the randomized controlled trial, credited to British epidemiologist Austin Bradford Hill, which revolutionized trial design. More recent conflicts, such as those in Vietnam, Iraq, and Afghanistan, have driven advancements in trauma care, heroin addiction treatment, and hemorrhage management. In response to historical abuses committed by the Nazis during WWII, the evolution of clinical trials has increasingly emphasized ethical standards, particularly informed consent, starting with the Doctors’ Trial and the Nuremberg Code. This article discusses how military needs and wartime innovations have shaped modern clinical research, highlighting the interplay between military imperatives and medical progress. Ultimately, clinical trials play an essential role in advancing medical science and improving patient outcomes.
Citation: Clinical Trials
PubDate: 2025-01-16T06:02:26Z
DOI: 10.1177/17407745241309054
- Evaluating the impact of stratification on the power and cross-arm balance
of randomized phase 2 clinical trials-
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Authors: Anna Moseley, Michael LeBlanc, Boris Freidlin, Rory M Shallis, Amer M Zeidan, David A Sallman, Harry P Erba, Richard F Little, Megan Othus; Michael LeBlanc, Boris Freidlin, Rory M Shallis, Amer M Zeidan, David A Sallman, Harry P Erba, Richard F Little, Megan Othus
Abstract: Clinical Trials, Ahead of Print.
Background/aimsRandomized clinical trials often use stratification to ensure balance between arms. Analysis of primary endpoints of these trials typically uses a “stratified analysis,” in which analyses are performed separately in each subgroup defined by the stratification factors, and those separate analyses are weighted and combined. In the phase 3 setting, stratified analyses based on a small number of stratification factors can provide a small increase in power. The impact on power and type-1 error of stratification in the setting of smaller sample sizes as in randomized phase 2 trials has not been well characterized.MethodsWe performed computational studies to characterize the power and cross-arm balance of modestly sized clinical trials (less than 170 patients) with varying numbers of stratification factors (0–6), sample sizes, randomization ratios (1:1 vs 2:1), and randomization methods (dynamic balancing vs stratified block).ResultsWe found that the power of unstratified analyses was minimally impacted by the number of stratification factors used in randomization. Analyses stratified by 1–3 factors maintained power over 80%, while power dropped below 80% when four or more stratification factors were used. These trends held regardless of sample size, randomization ratio, and randomization method. For a given randomization ratio and sample size, increasing the number of factors used in randomization had an adverse impact on cross-arm balance. Stratified block randomization performed worse than dynamic balancing with respect to cross-arm balance when three or more stratification factors were used.ConclusionStratified analyses can decrease power in the setting of phase 2 trials when the number of patients in a stratification subgroup is small.
Citation: Clinical Trials
PubDate: 2025-01-16T05:55:06Z
DOI: 10.1177/17407745241304065
- A multilevel framework for recruitment and retention in implementation
trials: An illustrative example-
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Authors: Nathaniel J Williams, Alexandra E Gomes, Nallely R Vega, Susan Esp, Mimi Choy-Brown, Rinad S Beidas; Alexandra E Gomes, Nallely R Vega, Susan Esp, Mimi Choy-Brown, Rinad S Beidas
Abstract: Clinical Trials, Ahead of Print.
Background:Implementation and hybrid effectiveness–implementation trials aspire to speed the translation of science into practice by generating crucial evidence for improving the uptake of effective health interventions. By design, they pose unique recruitment and retention challenges due to their aims, units of analysis, and sampling plans, which typically require many clinical sites (i.e. often 20 or more) and participation by individuals who are related across multiple levels (e.g. linked organizational leaders, clinicians, and patients). In this article, we present a new multilevel, theory-informed, and relationship-centered framework for conceptualizing recruitment and retention in implementation and hybrid effectiveness–implementation trials which integrates and builds on prior work on recruitment and retention strategies in patient-focused trials. We describe the framework’s application in the Working to Implement and Sustain Digital Outcome Measures hybrid type III trial, which occurred in part during the COVID-19 pandemic.Methods:Recruitment for the Working to Implement and Sustain Digital Outcome Measures trial occurred from October 2019 to February 2022. Development of recruitment and retention strategies was guided by a newly developed multilevel framework, which targeted the capability, opportunity, and motivation of organizational leaders, clinicians, patient-facing administrative staff, and patients to engage in research. A structured assessment guide was developed and applied to refine recruitment and retention approaches throughout the trial. We describe the framework and its application amid the onset of the COVID-19 pandemic which required rapid adjustments to address numerous barriers.Results:The Working to Implement and Sustain Digital Outcome Measures trial enrolled 21 outpatient clinics in three US states, incorporating 252 clinicians and 686 caregivers of youth (95% of patient recruitment target) across two distinct phases. Data completion rates for organizational leaders and clinicians averaged 90% over five waves spanning 18 months, despite the onset of the COVID pandemic. Caregiver completion rates of monthly follow-up assessments ranged from 80%–88% across 6 months. This article presents the multilevel framework, assessment guide, and strategies used to achieve recruitment and retention targets at each level.Conclusion:We conducted a multi-state hybrid type III effectiveness–implementation trial that maintained high recruitment and retention across all relevant levels amid a global pandemic. The newly developed multilevel recruitment and retention framework and assessment guide presented here, which integrates behavioral theory, a relationship-focused lens, and evidence-based strategies for participant recruitment and retention at multiple levels, can be adapted and used by other researchers for implementation, hybrid, and multilevel pragmatic trials as well as other implementation studies.
Citation: Clinical Trials
PubDate: 2025-01-11T06:11:15Z
DOI: 10.1177/17407745241307948
- Impact of correlation structure on sample size requirements of statistical
methods for multiple binary outcomes: A simulation study-
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Authors: Kanako Fuyama, Kentaro Sakamaki, Kohei Uemura, Isao Yokota; Kentaro Sakamaki, Kohei Uemura, Isao Yokota
Abstract: Clinical Trials, Ahead of Print.
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.
Citation: Clinical Trials
PubDate: 2025-01-03T11:43:07Z
DOI: 10.1177/17407745241304706
- A framework for sequential monitoring of individual N-of-1 trials and
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Authors: Subodh Selukar, David K Prince, Susanne May; David K Prince, Susanne May
Abstract: Clinical Trials, Ahead of Print.
Background: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.Methods: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.Results: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]-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.Conclusion: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.
Citation: Clinical Trials
PubDate: 2025-01-02T10:51:40Z
DOI: 10.1177/17407745241304284
- Exclusion of people from oncology clinical trials based on functional
status-
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Authors: Nicole D Agaronnik, Mary Linton B Peters, Lisa I Iezzoni; Mary Linton B Peters, Lisa I Iezzoni
Abstract: Clinical Trials, Ahead of Print.
Background/Aims: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.Methods: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.Results: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.Conclusion: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.
Citation: Clinical Trials
PubDate: 2025-01-02T10:42:20Z
DOI: 10.1177/17407745241304114
- Central statistical monitoring in clinical trial management: A scoping
review-
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Authors: Maciej Fronc, Michał Jakubczyk, Sharon B Love, Susan Talbot, Timothy Rolfe; Michał Jakubczyk, Sharon B Love, Susan Talbot, Timothy Rolfe
Abstract: Clinical Trials, Ahead of Print.
Background: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.Methods: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.Results: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.Discussion: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.
Citation: Clinical Trials
PubDate: 2025-01-02T10:35:19Z
DOI: 10.1177/17407745241304059
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