Clinical Trials最新文献

{"title":"2024 Peer Reviewers.","authors":"","doi":"10.1177/17407745251314089","DOIUrl":"https://doi.org/10.1177/17407745251314089","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"22 1","pages":"126-127"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381785","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":"Scaling and interpreting treatment effects in clinical trials using restricted mean survival time.","authors":"Theodore Karrison, Chen Hu, James Dignam","doi":"10.1177/17407745241254995","DOIUrl":"10.1177/17407745241254995","url":null,"abstract":"<p><strong>Background: </strong>Restricted mean survival time is the expected duration of survival up to a chosen time of restriction <math><mrow><mi>τ</mi></mrow></math>. For comparison studies, the difference in restricted mean survival times between two groups provides a summary measure of the treatment effect that is free of assumptions regarding the relative shape of the two survival curves, such as proportional hazards. However, it can be difficult to judge the magnitude of the effect from a comparison of restricted means due to the truncation of observation at time <math><mrow><mi>τ</mi></mrow></math>.</p><p><strong>Methods: </strong>In this article, we describe additional ways of expressing the treatment effect based on restricted means that can be helpful in this regard. These include the ratio of restricted means, the ratio of life-years (or time) lost, and the average integrated difference between the survival curves, equal to the difference in restricted means divided by <math><mrow><mi>τ</mi><mo>.</mo></mrow></math> These alternative metrics are straightforward to calculate and provide a means for scaling the effect size as an aid to interpretation. Examples from two randomized, multicenter clinical trials in prostate cancer, NRG/RTOG 0521 and NRG/RTOG 0534, with primary endpoints of overall survival and biochemical/radiological progression-free survival, respectively, are presented to illustrate the ideas.</p><p><strong>Results: </strong>The four effect measures (restricted mean survival time difference, restricted mean survival time ratio, time lost ratio, and average survival rate difference) were 0.45 years, 1.05, 0.81, and 0.038 for RTOG 0521 and 1.36 years, 1.17, 0.56, and 0.12 for RTOG 0534 with <math><mrow><mi>τ</mi></mrow></math> = 12 and 11 years, respectively. Thus, for example, the 0.45-year difference in the first trial translates into a 19% reduction in time lost and a 3.8% average absolute difference between the survival curves over the 12-year horizon, a modest effect size, whereas the 1.36-year difference in the second trial corresponds to a 44% reduction in time lost and a 12% absolute survival difference, a rather large effect.</p><p><strong>Conclusions: </strong>In addition to the difference in restricted mean survival times, these alternative measures can be helpful in determining whether the magnitude of the treatment effect is clinically meaningful.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"3-10"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141316830","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":"Optimizing accrual to a large-scale, clinically integrated randomized trial in anesthesiology: A 2-year analysis of recruitment.","authors":"Hanae K Tokita, Melissa Assel, Joanna Serafin, Emily Lin, Leslie Sarraf, Geema Masson, Tracy-Ann Moo, Jonas A Nelson, Brett A Simon, Andrew J Vickers","doi":"10.1177/17407745241255087","DOIUrl":"10.1177/17407745241255087","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Performing large randomized trials in anesthesiology is often challenging and costly. The clinically integrated randomized trial is characterized by simplified logistics embedded into routine clinical practice, enabling ease and efficiency of recruitment, offering an opportunity for clinicians to conduct large, high-quality randomized trials under low cost. Our aims were to (1) demonstrate the feasibility of the clinically integrated trial design in a high-volume anesthesiology practice and (2) assess whether trial quality improvement interventions led to more balanced accrual among study arms and improved trial compliance over time.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This is an interim analysis of recruitment to a cluster-randomized trial investigating three nerve block approaches for mastectomy with immediate implant-based reconstruction: paravertebral block (arm 1), paravertebral plus interpectoral plane blocks (arm 2), and serratus anterior plane plus interpectoral plane blocks (arm 3). We monitored accrual and consent rates, clinician compliance with the randomized treatment, and availability of outcome data. Assessment after the initial year of implementation showed a slight imbalance in study arms suggesting areas for improvement in trial compliance. Specific improvement interventions included increasing the frequency of communication with the consenting staff and providing direct feedback to clinician investigators about their individual recruitment patterns. We assessed overall accrual rates and tested for differences in accrual, consent, and compliance rates pre- and post-improvement interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Overall recruitment was extremely high, accruing close to 90% of the eligible population. In the pre-intervention period, there was evidence of bias in the proportion of patients being accrued and receiving the monthly block, with higher rates in arm 3 (90%) compared to arms 1 (81%) and 2 (79%, p = 0.021). In contrast, in the post-intervention period, there was no statistically significant difference between groups (p = 0.8). Eligible for randomization rate increased from 89% in the pre-intervention period to 95% in the post-intervention period (difference 5.7%; 95% confidence interval = 2.2%-9.4%, p = 0.002). Consent rate increased from 95% to 98% (difference of 3.7%; 95% confidence interval = 1.1%-6.3%; p = 0.004). Compliance with the randomized nerve block approach was maintained at close to 100% and availability of primary outcome data was 100%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The clinically integrated randomized trial design enables rapid trial accrual with a high participant compliance rate in a high-volume anesthesiology practice. Continuous monitoring of accrual, consent, and compliance rates is necessary to maintain and improve trial conduct and reduce potential biases. This trial methodology serves as a template for the implementation of other large, low-cost randomized","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"57-65"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141418251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials.","authors":"Guangyu Tong, Pascale Nevins, Mary Ryan, Kendra Davis-Plourde, Yongdong Ouyang, Jules Antoine Pereira Macedo, Can Meng, Xueqi Wang, Agnès Caille, Fan Li, Monica Taljaard","doi":"10.1177/17407745241276137","DOIUrl":"10.1177/17407745241276137","url":null,"abstract":"<p><strong>Background/aims: </strong>Stepped-wedge cluster randomized trials tend to require fewer clusters than standard parallel-arm designs due to the switches between control and intervention conditions, but there are no recommendations for the minimum number of clusters. Trials randomizing an extremely small number of clusters are not uncommon, but the justification for small numbers of clusters is often unclear and appropriate analysis is often lacking. In addition, stepped-wedge cluster randomized trials are methodologically more complex due to their longitudinal correlation structure, and ignoring the distinct within- and between-period intracluster correlations can underestimate the sample size in small stepped-wedge cluster randomized trials. We conducted a review of published small stepped-wedge cluster randomized trials to understand how and why they are used, and to characterize approaches used in their design and analysis.</p><p><strong>Methods: </strong>Electronic searches were used to identify primary reports of full-scale stepped-wedge cluster randomized trials published during the period 2016-2022; the subset that randomized two to six clusters was identified. Two reviewers independently extracted information from each report and any available protocol. Disagreements were resolved through discussion.</p><p><strong>Results: </strong>We identified 61 stepped-wedge cluster randomized trials that randomized two to six clusters: median sample size (Q1-Q3) 1426 (420-7553) participants. Twelve (19.7%) gave some indication that the evaluation was considered a \"preliminary\" evaluation and 16 (26.2%) recognized the small number of clusters as a limitation. Sixteen (26.2%) provided an explanation for the limited number of clusters: the need to minimize contamination (e.g. by merging adjacent units), limited availability of clusters, and logistical considerations were common explanations. Majority (51, 83.6%) presented sample size or power calculations, but only one assumed distinct within- and between-period intracluster correlations. Few (10, 16.4%) utilized restricted randomization methods; more than half (34, 55.7%) identified baseline imbalances. The most common statistical method for analysis was the generalized linear mixed model (44, 72.1%). Only four trials (6.6%) reported statistical analyses considering small numbers of clusters: one used generalized estimating equations with small-sample correction, two used generalized linear mixed model with small-sample correction, and one used Bayesian analysis. Another eight (13.1%) used fixed-effects regression, the performance of which requires further evaluation under stepped-wedge cluster randomized trials with small numbers of clusters. None used permutation tests or cluster-period level analysis.</p><p><strong>Conclusion: </strong>Methods appropriate for the design and analysis of small stepped-wedge cluster randomized trials have not been widely adopted in practice. Greater awareness","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"45-56"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11810615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142388716","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":"Composite endpoints in COVID-19 randomized controlled trials: a systematic review.","authors":"Pedro Nascimento Martins, Mateus Henrique Toledo Lourenço, Gabriel Paz Souza Mota, Alexandre Biasi Cavalcanti, Ana Carolina Peçanha Antonio, Fredi Alexander Diaz-Quijano","doi":"10.1177/17407745241276130","DOIUrl":"10.1177/17407745241276130","url":null,"abstract":"<p><strong>Background/aims: </strong>This study aimed to determine the prevalence of ordinal, binary, and numerical composite endpoints among coronavirus disease 2019 trials and the potential bias attributable to their use.</p><p><strong>Methods: </strong>We systematically reviewed the Cochrane COVID-19 Study Register to assess the prevalence, characteristics, and bias associated with using composite endpoints in coronavirus disease 2019 randomized clinical trials. We compared the effect measure (relative risk) of composite outcomes and that of its most critical component (i.e. death) by estimating the Bias Attributable to Composite Outcomes index [ln(relative risk for the composite outcome)/ln(relative risk for death)].</p><p><strong>Results: </strong>Composite endpoints accounted for 152 out of 417 primary endpoints in coronavirus disease 2019 randomized trials, being more frequent among studies published in high-impact journals. Ordinal endpoints were the most common (54% of all composites), followed by binary or time-to-event (34%), numerical (11%), and hierarchical (1%). Composites predominated among trials enrolling patients with severe disease when compared to trials with a mild or moderate case mix (odds ratio = 1.72). Adaptations of the seven-point World Health Organization scale occurred in 40% of the ordinal primary endpoints, which frequently underwent dichotomization for the statistical analyses. Mortality accounted for a median of 24% (interquartile range: 6%-48%) of all events when included in the composite. The median point estimate of the Bias Attributable to Composite Outcomes index was 0.3 (interquartile range: -0.1 to 0.7), being significantly lower than 1 in 5 of 24 comparisons.</p><p><strong>Discussion: </strong>Composite endpoints were used in a significant proportion of coronavirus disease 2019 trials, especially those involving severely ill patients. This is likely due to the higher anticipated rates of competing events, such as death, in such studies. Ordinal composites were common but often not fully appreciated, reducing the potential gains in information and statistical efficiency. For studies with binary composites, death was the most frequent component, and, unexpectedly, composite outcome estimates were often closer to the null when compared to those for mortality death. Numerical composites were less common, and only two trials used hierarchical endpoints. These newer approaches may offer advantages over traditional binary and ordinal composites; however, their potential benefits warrant further scrutiny.</p><p><strong>Conclusion: </strong>Composite endpoints accounted for more than a third of coronavirus disease 2019 trials' primary endpoints; their use was more common among studies that included patients with severe disease and their point effect estimates tended to underestimate those for mortality.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"77-87"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142399650","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":"Modeling impact of inflation reduction act price negotiations on new drug pipeline considering differential contributions of large and small biopharmaceutical companies.","authors":"Gregory Vaughan, Roger Du, Fred D Ledley","doi":"10.1177/17407745241259112","DOIUrl":"10.1177/17407745241259112","url":null,"abstract":"<p><strong>Background/aims: </strong>Provisions of the Inflation Reduction Act mandating drug price negotiation by the Centers for Medicare & Medicaid Services have been criticized as a threat to pharmaceutical innovation. This study models potential impacts of the Inflation Reduction Act on drug approvals based on the differential contributions of large pharmaceutical companies and smaller biotechnology firms to clinical trials and the availability of capital.</p><p><strong>Methods: </strong>This study examined research and development expense, revenue, and new investment (sale of common and preferred stock) by public biopharmaceutical companies and sponsorship of phased clinical trials in ClinicalTrials.gov. Financial data were incorporated in a model that estimates the number of drugs in each phase and approvals from reported phase-specific costs and transition rates, proportional sponsorship of trials by companies of different size, projected reductions in research and development spending based on company size, and three scenarios by which large companies may allocate reductions in research and development spending among clinical phases: (1) research and development proportionally reduced across phases; (2) research and development disproportionally reduced in phases 2-3; and (3) research and development disproportionately reduced in phases 1-2.</p><p><strong>Results: </strong>Financial data were examined for 1378 public biopharmaceutical companies (2000-2018). Research and development expense was associated with revenue for 79 large companies with market capitalization ≥$7 billion with a 10% reduction in revenue reducing research and development expense by 8.4%. For 1299 smaller companies with market capitalization <$7 billion, research and development was associated with new investment, but not revenue. Smaller companies sponsored 55.2% of phase 1, 55.6% of phase 2, and 49.8% of phase 3 trials in ClinicalTrials.gov 2013-2018. In a model of clinical development that apportions clinical trials between large and smaller companies and determines the number of trials based on research and development resources, 400 drugs entering development produced 47.3 approvals (11.83% rate). A 10% reduction in revenue, reflecting the upper boundary of observed changes 2000-2018, with (1) proportional reduction across phases 1-3 produced 45.1 approvals (4.61% reduction); (2) disproportional reduction of phases 2-3 produced 42.8 approvals (9.55% reduction); and (3) disproportional reduction of phases 1-2 produced 46.9 approvals (0.95% reduction).</p><p><strong>Conclusion: </strong>This work suggests that the drug price negotiation provisions of the Inflation Reduction Act could have little or no impact on the number of drug approvals. While large pharmaceutical companies may reduce research and development spending, continued research and development by smaller companies and strategic allocation of research and development resources by large companies may mi","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"88-99"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11809114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141757646","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 designing a randomized, double-blind noninferiority trial for treatment of acne: The SD-ACNE trial.","authors":"John S Barbieri, Susan Ellenberg, Elizabeth Grice, Ann Tierney, Suzette Baez VanderBeek, Maryte Papadopoulos, Jennifer Mason, Anabel Mason, James Dattilo, David J Margolis","doi":"10.1177/17407745241265094","DOIUrl":"10.1177/17407745241265094","url":null,"abstract":"<p><strong>Background/aims: </strong>Excessive use of antibiotics has led to development of antibiotic resistance and other antibiotic-associated complications. Dermatologists prescribe more antibiotics per clinician than any other major specialty, with much of this use for acne. Alternative acne treatments are available but are used much less often than antibiotics, at least partially because dermatologists feel that they are less effective. Spironolactone, a hormonal therapy with antiandrogen effects that can address the hormonal pathogenesis of acne, may represent a therapeutic alternative to oral antibiotics for women with acne. However, the comparative effects of spironolactone and oral antibiotics in the treatment of acne have not been definitively studied. The Spironolactone versus Doxycycline for Acne: A Comparative Effectiveness, Noninferiority Evaluation (SD-ACNE) trial aims to answer whether spironolactone, in addition to standard topical therapy, is noninferior to doxycycline (an oral antibiotic) for women with acne. Several interesting challenges arose in the development of this study, including determining acceptability of the comparative regimens to participating dermatologists, identifying data to support a noninferiority margin, and establishing a process for unblinding participants after they completed the study while maintaining the blind for study investigators.</p><p><strong>Methods: </strong>We present the scientific and clinical rationale for the decisions made in the design of the trial, including input from key stakeholders through a Delphi consensus process.</p><p><strong>Results: </strong>The Spironolactone versus Doxycycline for Acne: A Comparative Effectiveness, Noninferiority Evaluation trial (NCT04582383) is being conducted at a range of community and academic sites in the United States. To maximize external validity and inform clinical practice, the study is designed with broad eligibility criteria and no prohibition of use of topical medications. Participants in the trial will be randomized to receive either spironolactone 100 mg/day or doxycycline hyclate 100 mg/day for 16 weeks. The primary outcome is the absolute decrease in inflammatory lesion count, and we have established a noninferiority margin of four inflammatory lesions. Secondary outcomes include the percentage of participants achieving Investigator Global Assessment success, change in quality of life, and microbiome changes and diversity.</p><p><strong>Conclusions: </strong>The Spironolactone versus Doxycycline for Acne: A Comparative Effectiveness, Noninferiority Evaluation trial will have substantial implications for the treatment of acne and antibiotic stewardship. In addition, this study will provide important information on the effect of these systemic agents on the development of changes to the microbiome and antibiotic resistance in a healthy population of patients.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"66-76"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787516","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":"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":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We make some simplifying assumptions in order to calculate the variance: in particular that we recruit the same number of participants, &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;, 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 &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; for two individuals who present at the same time, to a value of &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; 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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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 &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; and &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; can be plotted as a contour map over this space. The shape of this variance surface depends on &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;τ&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; and on the parameter &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;-&lt;/mo&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;, 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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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":"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}