Clinical Trials最新文献

{"title":"Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials.","authors":"Guangyu Tong, Jiaqi Tong, Yi Jiang, Denise Esserman, Michael O Harhay, Joshua L Warren","doi":"10.1177/17407745231222018","DOIUrl":"10.1177/17407745231222018","url":null,"abstract":"<p><strong>Background: </strong>Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes.</p><p><strong>Methods: </strong>This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings.</p><p><strong>Results: </strong>Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity.</p><p><strong>Conclusion: </strong>We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"451-460"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11233424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139402217","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":"Assessing the current utilization status of wearable devices in clinical research.","authors":"Takashi Miyakoshi, Yoichi M Ito","doi":"10.1177/17407745241230287","DOIUrl":"10.1177/17407745241230287","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords \"ActiGraph,\"\"Apple Watch,\"\"Empatica,\"\"Fitbit,\"\"Garmin,\" and \"wearable devices\" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the fu","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"470-482"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140130893","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":"Public involvement in Australian clinical trials: A systematic review.","authors":"Tessa-May Zirnsak, Ashley H Ng, Catherine Brasier, Richard Gray","doi":"10.1177/17407745231224533","DOIUrl":"10.1177/17407745231224533","url":null,"abstract":"<p><strong>Background: </strong>Public involvement enhances the relevance, quality, and impact of research. There is some evidence that public involvement in Australian research lags other countries, such as the United Kingdom. The purpose of the systematic review was to establish the rates and describe the characteristics of public involvement in Australian clinical trials.</p><p><strong>Methods: </strong>We reviewed evidence of public involvement in all Australian randomised controlled trials published in the first 6 months of 2021. To determine the quality of public involvement, we used the five-item short-form version of the Guidance of Reporting Involvement Patients and the Public, version 2.</p><p><strong>Results: </strong>In total, 325 randomised controlled trials were included, of which 17 (5%) reported any public involvement. Six trials reported public involvement in setting the research aim and seven in developing study methods. The authors of one study reflected on the overall role and influence of public involvement in the research.</p><p><strong>Conclusion: </strong>Rate of public involvement in Australian clinical trials is seemingly substantially lower than those reported in countries with similar advanced public health care systems, notably the United Kingdom. Our observations may be explained by a lack of researcher skills in how to involve the public and the failure by major funding agencies in Australia to mandate public involvement when deciding on how to award grant funding.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"507-515"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139971180","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":"Response to Harrell's commentary.","authors":"Kelly Van Lancker, Frank Bretz, Oliver Dukes","doi":"10.1177/17407745241251851","DOIUrl":"10.1177/17407745241251851","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"415-417"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141199681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A safety estimand for late phase clinical trials where the analysis period varies over the subjects.","authors":"Katarina Hedman, Vera Lisovskaja, Per Nyström","doi":"10.1177/17407745241230933","DOIUrl":"10.1177/17407745241230933","url":null,"abstract":"<p><strong>Background/aims: </strong>Evaluating safety is as important as evaluating efficacy in a clinical trial, yet the tradition for safety analysis is rudimentary. This article explores more complex methodologies for safety evaluation, with the aim of improving the interpretability, as well as generalizability, of the results.</p><p><strong>Methods: </strong>For studies where the analysis periods vary over the subjects, using the International Council for Harmonisation estimand framework, we construct a formal estimand that could be used in the setting of safety surveillance that answers the clinical question of 'What is the magnitude of the increase in risk of experiencing an adverse event if the treatment is taken, as prescribed, for a specific period of time?'. Estimation methodologies for this estimand are also discussed.</p><p><strong>Results: </strong>The proposed estimand is similar to that found in the efficacy analyses of time to event data (e.g. in outcome studies), with the key difference of utilization of hypothetical intercurrent event strategy for the intercurrent event of treatment discontinuation. This is motivated by what we perceive to be a key difference for the safety objective compared to efficacy objectives, namely a desire for sensitivity (i.e. greater possibility of detecting a negative impact of the drug, if such exists) as opposed to the need to prove a positive effect of the drug in a conservative manner.</p><p><strong>Conclusion: </strong>It is valuable, and possible, to use the International Council for Harmonisation estimand framework not only for efficacy but also for safety evaluation, with the estimand driven by an interpretable, and relevant, clinical question.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"483-490"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139995742","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":"Research encouraging off-label use of quetiapine: A systematic meta-epidemiological analysis.","authors":"Peter Grabitz, Lana Saksone, Susanne Gabriele Schorr, Johannes Schwietering, Merlin Bittlinger, Jonathan Kimmelman","doi":"10.1177/17407745231225470","DOIUrl":"10.1177/17407745231225470","url":null,"abstract":"<p><strong>Background: </strong>Researchers often conduct small studies on testing a drug's efficacy in off-label indications. If positive results from these exploratory studies are not followed up by larger, randomized, double-blinded trials, physicians cannot be sure of a drug's clinical value. This may lead to off-label prescriptions of ineffective treatments. We aim to describe the way clinical studies fostered off-label prescription of the antipsychotic drug quetiapine (Seroquel).</p><p><strong>Methods: </strong>In this systematic meta-epidemiological analysis, we searched EMBASE, MEDLINE, Cochrane CENTRAL and PsycINFO databases and included clinical studies testing quetiapine for unapproved indications between May 1995 and May 2022. We then assessed the frequency with which publications providing low-level evidence suggesting efficacy of quetiapine for off-label indications was not followed up by large, randomized and double-blinded trials within 5 years.</p><p><strong>Results: </strong>In total, 176 published studies were identified that reported potential efficacy of quetiapine in at least 26 indications. Between 2000 and 2007, publication of exploratory studies suggesting promise for off-label indications rapidly outpaced publication of confirmatory trials. In the 24 indications with a minimum of 5 years of follow-up from the first positive exploratory study, 19 (79%) were not followed up with large confirmatory trials within 5 years. At least nine clinical practice guidelines recommend the use of quetiapine for seven off-label indications in which published confirmatory evidence is lacking.</p><p><strong>Conclusion: </strong>Many small, post-approval studies suggested the promise of quetiapine for numerous off-label indications. These findings generally went unconfirmed in large, blinded, randomized trials years after first being published. The imbalance of exploratory and confirmatory studies likely encourages ineffective off-label treatment.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"418-429"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139569962","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":"The use of linked administrative data in Australian randomised controlled trials: A scoping review.","authors":"Salma Fahridin, Neeru Agarwal, Karen Bracken, Stephen Law, Rachael L Morton","doi":"10.1177/17407745231225618","DOIUrl":"10.1177/17407745231225618","url":null,"abstract":"<p><strong>Background/aims: </strong>The demand for simplified data collection within trials to increase efficiency and reduce costs has led to broader interest in repurposing routinely collected administrative data for use in clinical trials research. The aim of this scoping review is to describe how and why administrative data have been used in Australian randomised controlled trial conduct and analyses, specifically the advantages and limitations of their use as well as barriers and enablers to accessing administrative data for use alongside randomised controlled trials.</p><p><strong>Methods: </strong>Databases were searched to November 2022. Randomised controlled trials were included if they accessed one or more Australian administrative data sets, where some or all trial participants were enrolled in Australia, and where the article was published between January 2000 and November 2022. Titles and abstracts were independently screened by two reviewers, and the full texts of selected studies were assessed against the eligibility criteria by two independent reviewers. Data were extracted from included articles by two reviewers using a data extraction tool.</p><p><strong>Results: </strong>Forty-one articles from 36 randomised controlled trials were included. Trial characteristics, including the sample size, disease area, population, and intervention, were varied; however, randomised controlled trials most commonly linked to government reimbursed claims data sets, hospital admissions data sets and birth/death registries, and the most common reason for linkage was to ascertain disease outcomes or survival status, and to track health service use. The majority of randomised controlled trials were able to achieve linkage in over 90% of trial participants; however, consent and participant withdrawals were common limitations to participant linkage. Reported advantages were the reliability and accuracy of the data, the ease of long term follow-up, and the use of established data linkage units. Common reported limitations were locating participants who had moved outside the jurisdictional area, missing data where consent was not provided, and unavailability of certain healthcare data.</p><p><strong>Conclusions: </strong>As linked administrative data are not intended for research purposes, detailed knowledge of the data sets is required by researchers, and the time delay in receiving the data is viewed as a barrier to its use. The lack of access to primary care data sets is viewed as a barrier to administrative data use; however, work to expand the number of healthcare data sets that can be linked has made it easier for researchers to access and use these data, which may have implications on how randomised controlled trials will be run in future.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"516-525"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139671451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary on van Lancker et al.","authors":"Frank E Harrell","doi":"10.1177/17407745241251609","DOIUrl":"10.1177/17407745241251609","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"412-414"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141199599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Covariate adjustment in randomized controlled trials: General concepts and practical considerations.","authors":"Kelly Van Lancker, Frank Bretz, Oliver Dukes","doi":"10.1177/17407745241251568","DOIUrl":"10.1177/17407745241251568","url":null,"abstract":"<p><p>There has been a growing interest in covariate adjustment in the analysis of randomized controlled trials in past years. For instance, the US Food and Drug Administration recently issued guidance that emphasizes the importance of distinguishing between conditional and marginal treatment effects. Although these effects may sometimes coincide in the context of linear models, this is not typically the case in other settings, and this distinction is often overlooked in clinical trial practice. Considering these developments, this article provides a review of when and how to use covariate adjustment to enhance precision in randomized controlled trials. We describe the differences between conditional and marginal estimands and stress the necessity of aligning statistical analysis methods with the chosen estimand. In addition, we highlight the potential misalignment of commonly used methods in estimating marginal treatment effects. We hereby advocate for the use of the standardization approach, as it can improve efficiency by leveraging the information contained in baseline covariates while remaining robust to model misspecification. Finally, we present practical considerations that have arisen in our respective consultations to further clarify the advantages and limitations of covariate adjustment.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"399-411"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141199600","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":"Assessing the impact of risk-based data monitoring on outcomes for a paediatric multicentre randomised controlled trial.","authors":"Renate Le Marsney, Kerry Johnson, Jenipher Chumbes Flores, Shelley Coetzer, Jennifer Darvas, Carmel Delzoppo, Arielle Jolly, Kate Masterson, Claire Sherring, Hannah Thomson, Endrias Ergetu, Patricia Gilholm, Kristen S Gibbons","doi":"10.1177/17407745231222019","DOIUrl":"10.1177/17407745231222019","url":null,"abstract":"<p><strong>Background/aims: </strong>Regulatory guidelines recommend that sponsors develop a risk-based approach to monitoring clinical trials. However, there is a lack of evidence to guide the effective implementation of monitoring activities encompassed in this approach. The aim of this study was to assess the efficiency and impact of the risk-based monitoring approach used for a multicentre randomised controlled trial comparing treatments in paediatric patients undergoing cardiac bypass surgery.</p><p><strong>Methods: </strong>This is a secondary analysis of data from a randomised controlled trial that implemented targeted source data verification as part of the risk-based monitoring approach. Monitoring duration and source to database error rates were calculated across the monitored trial dataset. The monitored and unmonitored trial dataset, and simulated trial datasets with differing degrees of source data verification and cohort sizes were compared for their effect on trial outcomes.</p><p><strong>Results: </strong>In total, 106,749 critical data points across 1,282 participants were verified from source data either remotely or on-site during the trial. The total time spent monitoring was 365 hours, with a median (interquartile range) of 10 (7, 16) minutes per participant. An overall source to database error rate of 3.1% was found, and this did not differ between treatment groups. A low rate of error was found for all outcomes undergoing 100% source data verification, with the exception of two secondary outcomes with error rates >10%. Minimal variation in trial outcomes were found between the unmonitored and monitored datasets. Reduced degrees of source data verification and reduced cohort sizes assessed using simulated trial datasets had minimal impact on trial outcomes.</p><p><strong>Conclusions: </strong>Targeted source data verification of data critical to trial outcomes, which carried with it a substantial time investment, did not have an impact on study outcomes in this trial. This evaluation of the cost-effectiveness of targeted source data verification contributes to the evidence-base regarding the context where reduced emphasis should be placed on source data verification as the foremost monitoring activity.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"461-469"},"PeriodicalIF":2.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139989555","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}