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":"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":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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 &lt;$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).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;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":"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":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;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":"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":"Adaptive promising zone design for sequential parallel comparison design with continuous outcomes.","authors":"Xinlin Lu, Guogen Shan","doi":"10.1177/17407745241309056","DOIUrl":"https://doi.org/10.1177/17407745241309056","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241309056"},"PeriodicalIF":2.2,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143036899","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":"Experiences with low-intervention clinical trials-the new category under the European Union Clinical Trials Regulation.","authors":"Amos J de Jong, Helga Gardarsdottir, Yared Santa-Ana-Tellez, Anthonius de Boer, Mira Gp Zuidgeest","doi":"10.1177/17407745241309293","DOIUrl":"https://doi.org/10.1177/17407745241309293","url":null,"abstract":"<p><strong>Background/aims: </strong>Low-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.</p><p><strong>Methods: </strong>We 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.</p><p><strong>Results: </strong>We 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.</p><p><strong>Conclusions: </strong>We 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.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241309293"},"PeriodicalIF":2.2,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143022383","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":"Military influences on the evolution of clinical trials throughout history.","authors":"Kamil Malshy, Alexis Steinmetz, Kit Yuen, Jathin Bandari, Ronald Rabinowitz","doi":"10.1177/17407745241309054","DOIUrl":"https://doi.org/10.1177/17407745241309054","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241309054"},"PeriodicalIF":2.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001576","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}