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LABORATORY AND EXPERIMENTAL MEDICINE (98 journals)

Showing 1 - 98 of 98 Journals sorted alphabetically
AAPS PharmSciTech     Hybrid Journal   (Followers: 9)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 7)
Adipocyte     Open Access   (Followers: 1)
African Journal of Laboratory Medicine     Open Access   (Followers: 2)
American Journal of Experimental and Clinical Research     Open Access   (Followers: 4)
American Journal of Medical and Biological Research     Open Access   (Followers: 11)
Animal Models and Experimental Medicine     Open Access  
Annals of Clinical Chemistry and Laboratory Medicine     Open Access   (Followers: 5)
Applied In Vitro Toxicology     Hybrid Journal   (Followers: 2)
Archives of Clinical and Experimental Medicine     Open Access  
Archives of Medical Research     Hybrid Journal   (Followers: 3)
Archives of Pathology & Laboratory Medicine     Full-text available via subscription   (Followers: 32)
Archives of Preventive Medicine     Open Access   (Followers: 3)
Biomedical Engineering     Hybrid Journal   (Followers: 3)
Bulletin of Experimental Biology and Medicine     Hybrid Journal  
Clinica Chimica Acta     Hybrid Journal   (Followers: 30)
Clinical & Experimental Metastasis     Hybrid Journal  
Clinical and Experimental Medical Journal     Full-text available via subscription   (Followers: 1)
Clinical and Experimental Medicine     Hybrid Journal   (Followers: 4)
Clinical Trials     Hybrid Journal   (Followers: 21)
Clinical Trials in Degenerative Diseases     Open Access  
Clinical Trials in Orthopedic Disorders     Open Access   (Followers: 1)
Current Medicine Research and Practice     Full-text available via subscription  
Current Research in Drug Discovery     Open Access   (Followers: 1)
Drug Design, Development and Therapy     Open Access   (Followers: 4)
Ecography     Hybrid Journal   (Followers: 27)
European Journal of Hospital Pharmacy : Science and Practice (EJHP)     Hybrid Journal   (Followers: 9)
European Journal of Medical Research     Open Access   (Followers: 1)
European Journal of Nanomedicine     Hybrid Journal   (Followers: 1)
Experimental & Molecular Medicine     Open Access   (Followers: 1)
Experimental Aging Research: An International Journal Devoted to the Scientific Study of the Aging Process     Hybrid Journal   (Followers: 3)
Experimental and Therapeutic Medicine     Full-text available via subscription   (Followers: 1)
Experimental Biology and Medicine     Hybrid Journal   (Followers: 3)
Expert Opinion on Drug Delivery     Hybrid Journal   (Followers: 20)
Frontiers in Laboratory Medicine     Open Access  
Frontiers in Medical Technology     Open Access   (Followers: 1)
IN VIVO     Full-text available via subscription   (Followers: 5)
International Archives of Biomedical and Clinical Research     Open Access  
International Journal of Experimental Pathology     Hybrid Journal   (Followers: 1)
International Journal of Health Research and Innovation     Open Access   (Followers: 2)
International Journal of Research in Medical Sciences     Open Access   (Followers: 7)
International Journal of Statistics in Medical Research     Hybrid Journal   (Followers: 5)
Journal of Cell Science & Therapy     Open Access   (Followers: 1)
Journal of Applied Biomaterials & Functional Materials     Hybrid Journal   (Followers: 1)
Journal of Biomedical and Clinical Research     Open Access  
Journal of Clinical Laboratory Analysis     Open Access   (Followers: 14)
Journal of Clinical Medicine and Research     Open Access  
Journal of Clinical Medicine Research     Open Access   (Followers: 4)
Journal of Clinical Trials     Open Access   (Followers: 6)
Journal of Current and Advance Medical Research     Open Access   (Followers: 2)
Journal of Current Medical Research and Practice     Open Access  
Journal of Current Research in Scientific Medicine     Open Access  
Journal of Drug Delivery and Therapeutics JDDT     Open Access   (Followers: 1)
Journal of Enzyme Inhibition and Medicinal Chemistry     Open Access   (Followers: 4)
Journal of Experimental & Clinical Medicine     Full-text available via subscription   (Followers: 1)
Journal of Experimental & Clinical Cancer Research     Open Access   (Followers: 2)
Journal of Experimental and Clinical Medicine     Open Access  
Journal of Experimental Medicine     Full-text available via subscription   (Followers: 46)
Journal of Experimental Pharmacology     Open Access   (Followers: 2)
Journal of Histotechnology     Hybrid Journal   (Followers: 2)
Journal of International Medical Research     Open Access   (Followers: 3)
Journal of Investigative Medicine High Impact Case Reports     Open Access  
Journal of Medicine and Biomedical Research     Open Access   (Followers: 1)
Journal of Muhammadiyah Medical Laboratory Technologist     Open Access  
Journal of Operating Department Practitioners     Full-text available via subscription   (Followers: 2)
Journal of the American Society of Cytopathology     Hybrid Journal   (Followers: 5)
Journal of Trace Elements in Medicine and Biology     Hybrid Journal   (Followers: 1)
Lab on a Chip     Full-text available via subscription   (Followers: 43)
Laboratory Investigation     Hybrid Journal   (Followers: 3)
Medical Devices & Sensors     Hybrid Journal  
Medical Image Analysis     Hybrid Journal   (Followers: 15)
Medical Instrumentation     Open Access  
Medical Laboratory Observer     Full-text available via subscription  
Medical Laboratory Technology Journal     Open Access  
Medicinal Chemistry Research     Hybrid Journal   (Followers: 12)
Medtech Insight     Full-text available via subscription   (Followers: 4)
Nanomedicine: Nanotechnology, Biology and Medicine     Hybrid Journal   (Followers: 7)
New Zealand Journal of Medical Laboratory Science     Full-text available via subscription   (Followers: 1)
Oriental Pharmacy and Experimental Medicine     Partially Free   (Followers: 3)
Pathology and Laboratory Medicine International     Open Access   (Followers: 7)
Physical Biology     Hybrid Journal   (Followers: 4)
Practical Laboratory Medicine     Open Access   (Followers: 2)
Proceedings of the Institution of Mechanical Engineers Part H: Journal of Engineering in Medicine     Hybrid Journal   (Followers: 3)
Prosthetics and Orthotics International     Hybrid Journal   (Followers: 10)
Pulse     Full-text available via subscription  
Qualitative Research in Medicine & Healthcare     Open Access  
Recent Advances in Biology and Medicine     Open Access  
Regulatory Toxicology and Pharmacology     Hybrid Journal   (Followers: 43)
Reproduction     Full-text available via subscription   (Followers: 7)
Revista Peruana de Medicina Experimental y Salud Pública     Open Access  
Revista Romana de Medicina de Laborator     Open Access  
RSC Medicinal Chemistry     Full-text available via subscription   (Followers: 6)
SA Pharmacist's Assistant     Open Access  
Savannah Journal of Medical Research and Practice     Full-text available via subscription  
SLAS Technology     Hybrid Journal   (Followers: 2)
Statistics in Medicine     Hybrid Journal   (Followers: 191)
Trends in Molecular Medicine     Full-text available via subscription   (Followers: 14)
Turkish Journal of Clinics and Laboratory     Open Access   (Followers: 1)
Similar Journals
Journal Cover
Clinical Trials
Journal Prestige (SJR): 2.399
Citation Impact (citeScore): 2
Number of Followers: 21  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1740-7745 - ISSN (Online) 1740-7753
Published by Sage Publications Homepage  [1099 journals]
  • Adapting isotonic dose-finding to a dynamic set of drug combinations with
           application to a phase I leukemia trial
    • Authors: Nolan A Wages, Daniel R Reed, Michael K Keng, Mark R Conaway, Gina R Petroni
      Abstract: Clinical Trials, Ahead of Print.
      Background/aimsThis article describes the proposed design of a phase I study evaluating the safety of ceramide nanoliposome and vinblastine among an initial set of 19 possible dose combinations in patients with relapsed/refractory acute myeloid leukemia and patients with untreated acute myeloid leukemia who are not candidates for intensive induction chemotherapy.MethodsExtensive collaboration between statisticians and clinical investigators revealed the need to incorporate several adaptive features into the design, including the flexibility of adding or eliminating certain dose combinations based on safety criteria applied to multiple dose pairs. During the design stage, additional dose levels of vinblastine were added, increasing the dimension of the drug combination space and thus the complexity of the problem. Increased complexity made application of existing drug combination dose-finding methods unsuitable in their current form.ResultsOur solution to these challenges was to adapt a method based on isotonic regression to meet the research objectives of the study. Application of this adapted method is described herein, and a simulation study of the design’s operating characteristics is conducted.ConclusionThe aim of this article is to bring to light examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying changing design conditions.
      Citation: Clinical Trials
      PubDate: 2021-01-11T06:24:57Z
      DOI: 10.1177/1740774520983484
       
  • Research Attitudes Questionnaire scores predict Alzheimer’s disease
           clinical trial dropout
    • Authors: Shana D Stites, R Scott Turner, Jeanine Gill, Anna Gurian, Jason Karlawish, Joshua D Grill
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundMissing data are a notable problem in Alzheimer’s disease clinical trials. One cause of missing data is participant dropout. The Research Attitudes Questionnaire is a 7-item instrument that measures an individual’s attitudes toward biomedical research, with higher scores indicating more favorable attitudes. The objective of this study was to describe the performance of the Research Attitudes Questionnaire over time and to examine whether Research Attitudes Questionnaire scores predict study dropout and other participant behaviors that affect trial integrity.MethodsThe Research Attitudes Questionnaire was collected at baseline and weeks 26 and 52 from each member of 119 participant/study partner dyads enrolled in a Phase 2, randomized, double-blind, placebo-controlled mild-to-moderate Alzheimer’s disease clinical trial. Within-subject longitudinal analyses examined change in Research Attitudes Questionnaire scores over time in each population. Logistic regression analyses that controlled for trial arm and clustering in trial sites were used to assess whether baseline Research Attitudes Questionnaire scores predicted trial completion, study medication compliance, and enrollment in optional substudies.ResultsParticipants and study partners endorsed statistically similar ratings on the Research Attitudes Questionnaire that were stable over time. Participants with baseline Research Attitudes Questionnaire scores above 28.5 were 4.7 (95% confidence interval = 1.01 to 21.95) times as likely to complete the trial compared to those with lower scores. Applying the same cutoff, baseline study partner Research Attitudes Questionnaire scores were similarly able to predict study completion (odds ratio = 4.2, 95% confidence interval = 1.71 to 10.32). Using a score cutoff of 27.5, higher participant Research Attitudes Questionnaire scores predicted study medication compliance (odds ratio = 5.85, 95% confidence interval = 1.34 to 25.54). No relationship was observed between Research Attitudes Questionnaire score and participation in optional substudies.ConclusionThis brief instrument that measures research attitudes may identify participants at risk for behaviors that cause missing data.
      Citation: Clinical Trials
      PubDate: 2021-01-11T06:21:25Z
      DOI: 10.1177/1740774520982315
       
  • Importance of incorporating quantitative imaging biomarker technical
           performance characteristics when estimating treatment effects
    • Authors: Nancy A Obuchowski, Erick M Remer, Ken Sakaie, Erika Schneider, Robert J Fox, Kunio Nakamura, Ricardo Avila, Alexander Guimaraes
      Abstract: Clinical Trials, Ahead of Print.
      Background/aimsQuantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected.MethodsMonte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease.ResultsIncorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (
      Citation: Clinical Trials
      PubDate: 2021-01-11T06:17:36Z
      DOI: 10.1177/1740774520981934
       
  • Lack of harmonization of coronavirus disease ordinal scales
    • Authors: Deborah A Zarin, Stephen Rosenfeld
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2020-12-16T07:44:17Z
      DOI: 10.1177/1740774520972082
       
  • Composite grading algorithm for the National Cancer Institute’s
           Patient-Reported Outcomes version of the Common Terminology Criteria for
           Adverse Events (PRO-CTCAE)
    • Authors: Ethan Basch, Claus Becker, Lauren J Rogak, Deborah Schrag, Bryce B Reeve, Patricia Spears, Mary Lou Smith, Mrinal M Gounder, Michelle R Mahoney, Gary K Schwartz, Antonia V Bennett, Tito R Mendoza, Charles S Cleeland, Jeff A Sloan, Deborah Watkins Bruner, Gisela Schwab, Thomas M Atkinson, Gita Thanarajasingam, Monica M Bertagnolli, Amylou C Dueck
      Abstract: Clinical Trials, Ahead of Print.
      Background:The Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events is an item library designed for eliciting patient-reported adverse events in oncology. For each adverse event, up to three individual items are scored for frequency, severity, and interference with daily activities. To align the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events with other standardized tools for adverse event assessment including the Common Terminology Criteria for Adverse Events, an algorithm for mapping individual items for any given adverse event to a single composite numerical grade was developed and tested.Methods:A five-step process was used: (1) All 179 possible Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events score combinations were presented to 20 clinical investigators to subjectively map combinations to single numerical grades ranging from 0 to 3. (2) Combinations with
      Citation: Clinical Trials
      PubDate: 2020-12-01T01:54:01Z
      DOI: 10.1177/1740774520975120
       
  • Data management in substance use disorder treatment research: Implications
           from data harmonization of National Institute on Drug Abuse-funded
           randomized controlled trials
    • Authors: Ryoko Susukida, Masoumeh Amin-Esmaeili, Evan Mayo-Wilson, Ramin Mojtabai
      Abstract: Clinical Trials, Ahead of Print.
      Background:Secondary analysis of data from completed randomized controlled trials is a critical and efficient way to maximize the potential benefits from past research. De-identified primary data from completed randomized controlled trials have been increasingly available in recent years; however, the lack of standardized data products is a major barrier to further use of these valuable data. Pre-statistical harmonization of data structure, variables, and codebooks across randomized controlled trials would facilitate secondary data analysis, including meta-analyses and comparative effectiveness studies. We describe a pre-statistical data harmonization initiative to standardize de-identified primary data from substance use disorder treatment randomized controlled trials funded by the National Institute on Drug Abuse available on the National Institute on Drug Abuse Data Share website.Methods:Standardized datasets and codebooks with consistent data structures, variable names, labels, and definitions were developed for 36 completed randomized controlled trials. Common data domains were identified to bundle data files from individual randomized controlled trials according to relevant concepts. Variables were harmonized if at least two randomized controlled trials used the same instruments. The structures of the harmonized data were determined based on the feedback from clinical trialists and substance use disorder research experts.Results:We have created a harmonized database of variables across 36 randomized controlled trials with a build-in label and a brief definition for each variable. Data files from the randomized controlled trials have been consistently categorized into eight domains (enrollment, demographics, adherence, adverse events, physical health measures, mental-behavioral-cognitive health measures, self-reported substance use measures, and biologic substance use measures). Standardized codebooks and concordance tables have also been developed to help identify instruments and variables of interest more easily.Conclusion:The harmonized data of randomized controlled trials of substance use disorder treatments can potentially promote future secondary data analysis of completed randomized controlled trials, allowing combining data from multiple randomized controlled trials and provide guidance for future randomized controlled trials in substance use disorder treatment research.
      Citation: Clinical Trials
      PubDate: 2020-12-01T01:53:36Z
      DOI: 10.1177/1740774520972687
       
  • Clinical trial monitoring effectiveness: Remote risk-based monitoring
           versus on-site monitoring with 100% source data verification
    • Authors: Osamu Yamada, Shih-Wei Chiu, Munenori Takata, Michiaki Abe, Mutsumi Shoji, Eri Kyotani, Chiyo Endo, Minami Shimada, Yuko Tamura, Takuhiro Yamaguchi
      Abstract: Clinical Trials, Ahead of Print.
      Background/Aims:Traditional on-site monitoring of clinical trials via frequent site visits and 100% source data verification is cost-consuming, and it still cannot guarantee data quality effectively. Depending on the types and designs of clinical trials, an alternative would be combining several monitoring methods, such as risk-based monitoring and remote monitoring. However, there is insufficient evidence of its effectiveness. This research compared the effectiveness of risk-based monitoring with a remote monitoring system with that of traditional on-site monitoring.Methods:With a cloud-based remote monitoring system called beagle View®, we created a remote risk-based monitoring methodology that focused only on critical data and processes. We selected a randomized controlled trial conducted at Tohoku University Hospital and randomly sampled 11 subjects whose case report forms had already been reviewed by data managers. Critical data and processes were verified retrospectively by remote risk-based monitoring; later, all data and processes were confirmed by on-site monitoring. We compared the ability of remote risk-based monitoring to detect critical data and process errors with that of on-site monitoring with 100% source data verification, including an examination of clinical trial staff workload and potential cost savings.Results:Of the total data points (n = 5617), 19.7% (n = 1105, 95% confidence interval = 18.7–20.7) were identified as critical. The error rates of critical data detected by on-site monitoring, remote risk-based monitoring, and data review by data managers were 7.6% (n = 84, 95% CI = 6.2–9.3), 7.6% (n = 84, 95% confidence interval = 6.2–9.3), and 3.9% (n = 43, 95% confidence interval = 2.9–5.2), respectively. The total number of critical process errors detected by on-site monitoring was 14. Of these 14, 92.9% (n = 13, 95% confidence interval = 68.5–98.7) and 42.9% (n = 6, 95% confidence interval = 21.4–67.4) of critical process errors were detected by remote risk-based monitoring and data review by data managers, respectively. The mean time clinical trial staff spent dealing with remote risk-based monitoring was 9.9 ± 5.3 (mean ± SD) min per visit per subject. Our calculations show that remote risk-based monitoring saved between 9 and 41 on-site monitoring visits, corresponding to a cost of between US$13,500 and US$61,500 per trial site.Conclusion:Remote risk-based monitoring was able to detect critical data and process errors as well as on-site monitoring with 100% source data verification, saving travel time and monitoring costs. Remote risk-based monitoring offers an effective alternative to traditional on-site monitoring of clinical trials.
      Citation: Clinical Trials
      PubDate: 2020-12-01T01:53:21Z
      DOI: 10.1177/1740774520971254
       
  • Meta-analysis of rare adverse events in randomized clinical trials:
           Bayesian and frequentist methods
    • Authors: Hwanhee Hong, Chenguang Wang, Gary L Rosner
      Abstract: Clinical Trials, Ahead of Print.
      Background/aims:Regulatory approval of a drug or device involves an assessment of not only the benefits but also the risks of adverse events associated with the therapeutic agent. Although randomized controlled trials (RCTs) are the gold standard for evaluating effectiveness, the number of treated patients in a single RCT may not be enough to detect a rare but serious side effect of the treatment. Meta-analysis plays an important role in the evaluation of the safety of medical products and has advantage over analyzing a single RCT when estimating the rate of adverse events.Methods:In this article, we compare 15 widely used meta-analysis models under both Bayesian and frequentist frameworks when outcomes are extremely infrequent or rare. We present extensive simulation study results and then apply these methods to a real meta-analysis that considers RCTs investigating the effect of rosiglitazone on the risks of myocardial infarction and of death from cardiovascular causes.Results:Our simulation studies suggest that the beta hyperprior method modeling treatment group-specific parameters and accounting for heterogeneity performs the best. Most models ignoring between-study heterogeneity give poor coverage probability when such heterogeneity exists. In the data analysis, different methods provide a wide range of log odds ratio estimates between rosiglitazone and control treatments with a mixed conclusion on their statistical significance based on 95% confidence (or credible) intervals.Conclusion:In the rare event setting, treatment effect estimates obtained from traditional meta-analytic methods may be biased and provide poor coverage probability. This trend worsens when the data have large between-study heterogeneity. In general, we recommend methods that first estimate the summaries of treatment-specific risks across studies and then relative treatment effects based on the summaries when appropriate. Furthermore, we recommend fitting various methods, comparing the results and model performance, and investigating any significant discrepancies among them.
      Citation: Clinical Trials
      PubDate: 2020-12-01T01:53:07Z
      DOI: 10.1177/1740774520969136
       
  • Assessing the potential for prevention or earlier detection of on-site
           monitoring findings from randomised controlled trials: Further analyses of
           findings from the prospective TEMPER triggered monitoring study
    • Authors: William J Cragg, Caroline Hurley, Victoria Yorke-Edwards, Sally P Stenning
      Abstract: Clinical Trials, Ahead of Print.
      Background/Aims:Clinical trials should be designed and managed to minimise important errors with potential to compromise patient safety or data integrity, employ monitoring practices that detect and correct important errors quickly, and take robust action to prevent repetition. Regulators highlight the use of risk-based monitoring, making greater use of centralised monitoring and reducing reliance on centre visits. The TEMPER study was a prospective evaluation of triggered monitoring (a risk-based monitoring method), whereby centres are prioritised for visits based on central monitoring results. Conducted in three UK-based randomised cancer treatment trials of investigational medicine products with time-to-event outcomes, it found high levels of serious findings at triggered centre visits but also at visits to matched control centres that, based on central monitoring, were not of concern. Here, we report a detailed review of the serious findings from TEMPER centre visits. We sought to identify feasible, centralised processes which might detect or prevent these findings without a centre visit.Methods:The primary outcome of this study was the proportion of all ‘major’ and ‘critical’ TEMPER centre visit findings theoretically detectable or preventable through a feasible, centralised process. To devise processes, we considered a representative example of each finding type through an internal consensus exercise. This involved (a) agreeing the potential, by some described process, for each finding type to be centrally detected or prevented and (b) agreeing a proposed feasibility score for each proposed process. To further assess feasibility, we ran a consultation exercise, whereby the proposed processes were reviewed and rated for feasibility by invited external trialists.Results:In TEMPER, 312 major or critical findings were identified at 94 visits. These findings comprised 120 distinct issues, for which we proposed 56 different centralised processes. Following independent review of the feasibility of the proposed processes by 87 consultation respondents across eight different trial stakeholder groups, we conclude that 306/312 (98%) findings could theoretically be prevented or identified centrally. Of the processes deemed feasible, those relating to informed consent could have the most impact. Of processes not currently deemed feasible, those involving use of electronic health records are among those with the largest potential benefit.Conclusions:This work presents a best-case scenario, where a large majority of monitoring findings were deemed theoretically preventable or detectable by central processes. Caveats include the cost of applying all necessary methods, and the resource implications of enhanced central monitoring for both centre and trials unit staff. Our results will inform future monitoring plans and emphasise the importance of continued critical review of monitoring processes and outcomes to ensure they remain appropriate.
      Citation: Clinical Trials
      PubDate: 2020-11-24T10:35:35Z
      DOI: 10.1177/1740774520972650
       
  • Event-specific win ratios and testing with terminal and non-terminal
           events
    • Authors: Song Yang, James Troendle
      Abstract: Clinical Trials, Ahead of Print.
      Background/aimsIn clinical trials, the primary outcome is often a composite endpoint defined as time to the first occurrence of either death or certain non-fatal events. Thus, a portion of available data would be omitted. In the win ratio approach, priorities are given to the clinically more important events, and more data are used. However, its power may be low if the treatment effect is predominantly on the non-terminal event.MethodsWe propose event-specific win ratios obtained separately on the terminal and non-terminal events. They can then be used to form global tests such as a linear combination test, the maximum test, or a [math] test.ResultsIn simulations, these tests often improve the power of the original win ratio test. Furthermore, when the terminal and non-terminal events experience differential treatment effects, the new tests are often more powerful than the log-rank test for the composite outcome. Whether the treatment effect is primarily on the terminal events or not, the new tests based on the event-specific win ratios can be useful when different types of events are present. The new tests can reject the null hypothesis of no difference in the event distributions in the two treatment arms with the terminal event showing detrimental effect and the non-terminal event showing beneficial effect. The maximum test and the [math] test do not have test-estimation coherency, but the maximum test has the coherency that the global null is rejected if and only if the null for one of the event types is rejected. When applied to data from the trial Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function (TOPCAT), the new tests all reject the null hypothesis of no treatment effect while both the log-rank test used in TOPCAT and the original win ratio approach show non-significant p-values.ConclusionWhether the treatment effect is primarily on the terminal events or the non-terminal events, the maximum test based on the event-specific win ratios can be a useful alternative for testing treatment effect in clinical trials with time-to-event outcomes when different types of events are present.
      Citation: Clinical Trials
      PubDate: 2020-11-24T10:35:15Z
      DOI: 10.1177/1740774520972408
       
  • Online monitoring of patient self-reported adverse events in early phase
           clinical trials: Views from patients, clinicians, and trial staff
    • Authors: Fiona Kennedy, Leanne Shearsmith, Michael Ayres, Oana C Lindner, Lewis Marston, Alison Pass, Sarah Danson, Galina Velikova
      Abstract: Clinical Trials, Ahead of Print.
      Background/aimsNew classes of cancer drugs bring a range of unknown and undesirable adverse events. Adverse event monitoring is essential in phase I trials to assess toxicity and safety. In phase II, the focus is also on efficacy but robust data on adverse events continue to inform the safety and the adverse event profile. Standard, clinician-led monitoring has been shown to underestimate patients’ symptoms. Hence, patient-reported adverse event monitoring has been argued to complement and improve the information on adverse events in early phase clinical trials. With advances in information technology, real-time patient self-reported adverse events in trials are feasible. This study explored the experiences and procedures for reporting adverse events in early phase trials among patients, clinical staff, and trial staff, and their views on using an electronic patient-reported outcome adverse event system in this setting.MethodsQualitative interviews were conducted with patients, purposively sampled across ages, gender, and different phases of trials, and with clinical and trial-related staff involved in early phase trials (e.g. consultants, research nurses, hospital-based trial assistants/data managers, trial unit management staff). Interviews explored patient experiences and views on current adverse event reporting processes and electronic patient-reported outcome adverse event reporting. Framework analysis techniques were used to analyse the data.ResultsInterviewees were from two hospital trusts with early phase portfolios in England and a trial unit, and included sixteen patients, five consultants, four research nurses, five hospital-based trial staff, and two trial unit staff. Interviews identified three key themes (patient experiences, data flow, and views on electronic patient-reported outcome adverse event reporting). Stakeholders emphasised the intensity of trials for patients and the importance of extensive information provision within the uncertainty of early phase trial drugs. Regular face-to-face appointments for patients supplemented by telephone contact aimed to capture any adverse events. Delayed or under-reporting of mild- or low-severity symptoms was evident among patients. Hospital-based staff highlighted the challenges of current data collection including intense timescales, monitoring by trial sponsors, and high workload. Positive views on electronic patient-reported outcome adverse events highlighted that this could provide a more comprehensive and accurate view on the side effects of new drugs. Clinical staff emphasised patient safety and the need for clear responsibilities for monitoring. The need for careful decision-making about data flow and symptom attribution was highlighted; with trial unit staff emphasising the need for clinician review.ConclusionTechnology advances mean it is timely to explore the benefits and challenges of electronic patient-reported outcome adverse event reporting. This is a complex area warranting further consideration within the trial community. We have developed an online patient self-reporting tool and a small pilot with early phase trial patients is underway.
      Citation: Clinical Trials
      PubDate: 2020-11-24T10:31:49Z
      DOI: 10.1177/1740774520972125
       
  • Recruitment and retention in randomized controlled trials with urban
           
    • Authors: Daniel L Dickerson, Jennifer Parker, Carrie L Johnson, Ryan A Brown, Elizabeth J D’Amico
      Abstract: Clinical Trials, Ahead of Print.
      Background:Although the majority of American Indians/Alaska Natives reside in urban areas, there are very few randomized controlled trials analyzing culturally centered substance use prevention interventions for this population.Methods:We describe methods employed to recruit and retain urban American Indian/Alaska Native adolescents into a randomized controlled trial, which was focused on testing the potential benefits of a substance use prevention intervention for this population. We also report challenges encountered in recruitment and retention of participants and strategies employed addressing these challenges. Data collection occurred from August 2014 to October 2017.Results:We partnered with two community-based organizations in different cities in California. We utilized American Indian/Alaska Native recruiters from communities, placed flyers in community-based organizations, and asked organizations to post flyers on their web and social media sites. We also offered gift cards for participants. Our initial recruitment and retention model was moderately successful; however, we encountered five main challenges: (1) transportation, (2) increasing trust and interest, (3) adding research sites, (4) getting the word out about the project, and (5) getting youth to complete follow-up surveys. Strategies employed to overcome transportation challenges included shortening the number of sessions, offering sessions on both weekends and weekdays, and increasing bus tokens and transportation options. We hired more staff from American Indian/Alaska Native communities, added more research sites from our previously established relationships, and were more proactive in getting the word out on the project in American Indian/Alaska Native communities. We also utilized more field tracking and emailed and mailed survey invitations to reach more participants for their follow-up surveys. Because of our efforts, we were nearly able to reach our initial recruitment and retention goals.Conclusion:Although our research team had previously established relationships with various urban American Indian/Alaska Native communities, we encountered various recruitment and retention challenges in our study. However, by identifying challenges and employing culturally appropriate strategies, we were able to collect valuable data on the potential effectiveness of a substance use prevention intervention for urban American Indian/Alaska Native adolescents. Findings from this study assist toward the development of potentially successful strategies to successfully recruit and retain urban American Indian/Alaska Native adolescents in randomized controlled trials.
      Citation: Clinical Trials
      PubDate: 2020-11-24T10:31:29Z
      DOI: 10.1177/1740774520971774
       
  • Details of risk–benefit communication in informed consent documents
           for phase I/II trials
    • Authors: Hannes Kahrass, Sabine Bossert, Christopher Schürmann, Daniel Strech
      Abstract: Clinical Trials, Ahead of Print.
      Background:Informed consent documents for clinical studies should disclose all reasonably foreseeable risks and benefits. Little guidance exists on how to navigate the complexities of risk–benefit communication, especially in early clinical research. Practice-oriented development of such guidance should be informed by evidence on what and how details of risks and benefits are currently communicated.Method:We surveyed the responsible parties of phase I/II trials registered in ClinicalTrials.gov that started 2007 or later and completed between 2012 and 2016 to sample informed consent documents from a broad spectrum of early phase clinical trials. Based on an assessment matrix, we qualitatively and quantitatively assessed the informed consent documents for details of risk–benefit communication.Results:The risk–benefit communication in the 172 informed consent documents differed substantially in several regards. The outcome, extent, and likelihood of health risks, for example, were described in 83%, 32%, and 63% of the informed consent documents. Only 45% of informed consent documents specified the outcome of mentioned health benefits, and the extent and likelihood of health benefits were never specified. From those informed consent documents reporting risk likelihoods, only 57% added frequency numbers to words such as “common” or “rare,” and even in these cases, we found strong variations for presented frequency ranges. Substantial heterogeneity also exists for how informed consent documents communicate other risk and benefit types and related safeguards.Conclusion:Our study points to several shortcomings and heterogeneities in how informed consent documents communicate risks and benefits to potential research participants. Health risks, for example, should be specified with frequency numbers, and health benefits should be specified at least by mentioning their outcomes. Further demand for research and policy development is needed to harmonize risk–benefit communication and to clarify ways to specify the likelihood of health benefits.
      Citation: Clinical Trials
      PubDate: 2020-11-24T10:31:10Z
      DOI: 10.1177/1740774520971770
       
  • The sublime inertia of informed consent language in early phase clinical
           trials involving patients
    • Authors: Jonathan Kimmelman
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2020-11-24T10:30:45Z
      DOI: 10.1177/1740774520971767
       
  • Bayesian biomarker-driven outcome-adaptive randomization with an imperfect
           biomarker assay
    • Authors: Leandro Garcia Barrado, Tomasz Burzykowski
      Abstract: Clinical Trials, Ahead of Print.
      Objective:We investigate the impact of biomarker assay’s accuracy on the operating characteristics of a Bayesian biomarker-driven outcome-adaptive randomization design.Methods:In a simulation study, we assume a trial with two treatments, two biomarker-based strata, and a binary clinical outcome (response). Pbt denotes the probability of response for treatment t (t = 0 or 1) in biomarker stratum (b = 0 or 1). Four different scenarios in terms of true underlying response probabilities are considered: a null (P00 = P01 = 0.25, P10 = P11= 0.25) and consistent (P00 = P10 = 0.25, P01 = 0.5) treatment effect scenario, as well as a quantitative (P00 = P01 = P10 = 0.25, P11 = 0.5) and a qualitative (P00 = P11 = 0.5, P01 = P10 = 0.25) stratum-treatment interaction. For each scenario, we compare the case of a perfect with the case of an imperfect biomarker assay with sensitivity and specificity of 0.8 and 0.7, respectively. In addition, biomarker-positive prevalence values P(B = 1) = 0.2 and 0.5 are investigated.Results:Results show that the use of an imperfect assay affects the operational characteristics of the Bayesian biomarker-based outcome-adaptive randomization design. In particular, the misclassification causes a substantial reduction in power accompanied by a considerable increase in the type-I error probability. The magnitude of these effects depends on the sensitivity and specificity of the assay, as well as on the distribution of the biomarker in the patient population.Conclusion:With an imperfect biomarker assay, the decision to apply a biomarker-based outcome-adaptive randomization design may require careful reflection.
      Citation: Clinical Trials
      PubDate: 2020-11-24T10:30:06Z
      DOI: 10.1177/1740774520964202
       
  • A Bayesian response-adaptive dose-finding and comparative effectiveness
           trial
    • Authors: Anna Heath, Maryna Yaskina, Petros Pechlivanoglou, David Rios, Martin Offringa, Terry P Klassen, Naveen Poonai, Eleanor Pullenayegum
      Abstract: Clinical Trials, Ahead of Print.
      Background/Aims:Combinations of treatments that have already received regulatory approval can offer additional benefit over Each of the treatments individually. However, trials of these combinations are lower priority than those that develop novel therapies, which can restrict funding, timelines and patient availability. This article develops a novel trial design to facilitate the evaluation of New combination therapies. This trial design combines elements of phase II and phase III trials to reduce the burden of evaluating combination therapies, while also maintaining a feasible sample size. This design was developed for a randomised trial that compares the properties of three combination doses of ketamine and dexmedetomidine, given intranasally, to ketamine delivered intravenously for children undergoing a closed reduction for a fracture or dislocation.Methods:This trial design uses response-adaptive randomisation to evaluate different dose combinations and increase the information collected for successful novel drug combinations. The design then uses Bayesian dose-response modelling to undertake a comparative effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods determine the thresholds for adapting the trial and making conclusions. We also used simulations to evaluate the probability of selecting the dose combination with the highest true effectiveness the operating characteristics of the design and its Bayesian predictive power.Results:With 410 participants, five interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correcting concluding non-inferiority, when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a greater than 77% chance of meeting its dual aims of dose-finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%.Conclusions:By simultaneously determining the optimal dose and collecting data on the relative effectiveness of an intervention, we can minimise administrative burden and recruitment time for a trial. This will minimise the time required to get effective, safe combination therapies to patients quickly. The proposed trial has high potential to meet the dual study objectives within a feasible overall sample size.
      Citation: Clinical Trials
      PubDate: 2020-11-24T10:29:46Z
      DOI: 10.1177/1740774520965173
       
  • Experience of conducting clinical trials of investigational medicinal
           products during a respiratory virus pandemic: Lessons learnt from COVID-19
           
    • Authors: James E Burns, Ffion E Carlin
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2020-11-24T10:06:55Z
      DOI: 10.1177/1740774520974113
       
  • Commentary on Willan & Thabane: Bayesian methods are valuable in pilot
           trials but the choice of prior needs careful consideration
    • Authors: Richard A Parker
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2020-11-24T10:06:16Z
      DOI: 10.1177/1740774520974976
       
  • A survey of the feasibility of developing osteoporosis clinical trials in
           Duchenne muscular dystrophy: Survey of the opinion of young people with
           Duchenne muscular dystrophy, families and clinicians
    • Authors: Sze Choong Wong, Shuko Joseph, Nadia Capaldi, Marina Di Marco, Jennifer Dunne, Michela Guglieri, Iain Horrocks, Volker Straub, S Faisal Ahmed
      Abstract: Clinical Trials, Ahead of Print.
      Background/aimsGiven the extent of osteoporosis in people with Duchenne muscular dystrophy treated with glucocorticoids and the limited evidence of bone-protective therapies, clinical trials are needed. We conducted surveys to obtain the opinion of young people with Duchenne muscular dystrophy, parents/guardians and neuromuscular clinicians on the feasibility of osteoporosis clinical trials in this population.MethodsOnline surveys were sent to three groups: (a) people with a confirmed diagnosis of Duchenne muscular dystrophy (≥14 years), (b) parents and guardians and (c) neuromuscular clinicians in the UK NorthStar Clinical Network. Surveys (a) and (b) were distributed via the UK Duchenne muscular dystrophy Registry.ResultsSurvey respondents included 52 people with Duchenne muscular dystrophy with a median age of 17 years (range: 14, 40) and 183 parents/guardians. Fourteen out of 23 (61%) NorthStar centres responded. Of the 52 people with Duchenne muscular dystrophy, 13 (25%) were very concerned about their bone health and 21 (40%) were slightly concerned. Of the 183 parents/guardians, 75 (41%) were very concerned about their son’s bone health and 90 (49%) were slightly concerned. Fractures and quality of life were the top two main outcome measures identified by people with Duchenne muscular dystrophy. Fractures and bone density were the top two main outcome measures identified by parents/guardians and neuromuscular clinicians. Thirty percent of people with Duchenne muscular dystrophy and 40% of parents/guardians would not take part if an osteoporosis trial involved a placebo that was administered parenterally. Only 2 of the 14 NorthStar centres (14%) would enrol people with Duchenne muscular dystrophy if a parenteral placebo was used in an osteoporosis trial in Duchenne muscular dystrophy.ConclusionThere is great awareness of bone health and the need for bone-protective trials among people with Duchenne muscular dystrophy and their carers. However, a proportion of people with Duchenne muscular dystrophy and parents are reluctant to participate in a placebo-controlled osteoporosis trial that included a parenteral therapy. A larger proportion of health care experts are unwilling to enrol their patients in such a trial. Our finding is relevant for the design of bone-protective studies in Duchenne muscular dystrophy.
      Citation: Clinical Trials
      PubDate: 2020-10-05T04:49:50Z
      DOI: 10.1177/1740774520958395
       
  • Adverse event load, onset, and maximum grade: A novel method of reporting
           adverse events in cancer clinical trials
    • Authors: Guilherme S Lopes, Christophe Tournigand, Curtis L Olswold, Romain Cohen, Emmanuelle Kempf, Leonard Saltz, Richard M Goldberg, Herbert Hurwitz, Charles Fuchs, Aimery de Gramont, Qian Shi
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundCurrent adverse event reporting practices do not document longitudinal characteristics of adverse effects, and alternative methods are not easily interpretable and have not been employed by clinical trials. Introducing time parameters in the evaluation of safety that are comprehensive yet easily interpretable could allow for a better understanding of treatment quality. In this study, we developed and applied a novel adverse event reporting method based on longitudinal adverse event changes to aid describing, summarizing, and presenting adverse event profile. We termed it the “Adverse Event Load, Onset, and Maximum Grade” method.MethodsWe developed two adverse event summary metrics to complement the traditional maximum grade report. Onset time indicates the time period in which the maximum grade for a specific adverse event occurred and was defined as “early” (i.e. maximum grade happened for the first time before 6 weeks) or “late” (i.e. after the 6th week). Adverse event load indicates the overall severity of a specific adverse event over the entire treatment. Higher adverse event load indicates a worse overall experience. These metrics can be calculated for adverse events with different maximum grades, in treatments with planned changes (e.g. dosage changes), using data sets with different number of adverse event data points between treatments (e.g. treatments with longer cycle lengths may have less adverse event data points) and on data sets with different adverse event data availability (e.g. cycle basis and patient-outcome reports). We tested the utility of this method using individual patient data from two major backbone therapies (“Irinotecan” and “Oxaliplatin”) from the N9741 trial available in the Fondation ARCAD database (fondationarcad.org). We investigated profiles of diarrhea, neutropenia/leukopenia, and nausea/vomiting.ResultsOur method provided additional information compared to traditional adverse event reports. For example, for nausea/vomiting, while patients in Irinotecan had a higher risk of experiencing maximum grade 3–4 (15.6% vs 7.6%, respectively; p 
      Citation: Clinical Trials
      PubDate: 2020-10-01T07:54:07Z
      DOI: 10.1177/1740774520959313
       
  • Use of electronic recruitment methods in a clinical trial of adults with
           gout
    • Authors: Hailey N Miller, Jeanne Charleston, Beiwen Wu, Kelly Gleason, Karen White, Cheryl R Dennison Himmelfarb, Daniel E Ford, Timothy B Plante, Allan C Gelber, Lawrence J Appel, Edgar R Miller, Stephen P Juraschek
      Abstract: Clinical Trials, Ahead of Print.
      Background/aims:Electronic-based recruitment methods are increasingly utilized in clinical trials to recruit and enroll research participants. The cost-effectiveness of electronic-based methods and impact on sample generalizability is unknown. We compared recruitment yields, cost-effectiveness, and demographic characteristics across several electronic and traditional recruitment methods.Methods:We analyzed data from the diet gout trial recruitment campaign. The diet gout trial was a randomized, controlled, cross-over trial that examined the effects of a dietary approaches to stop hypertension (DASH)–like diet on uric acid levels in adults with gout. We used four electronic medical record and four non-electronic medical record–based recruitment methods to identify and recruit potentially eligible participants. We calculated the response rate, screening visit completion rate, and randomization rate for each method. We also determined cost per response, the screening, and randomization for each method. Finally, we compared the demographic characteristics among individuals who completed the screening visit by recruitment method.Results:Of the 294 adults who responded to the recruitment campaign, 51% were identified from electronic medical record–based methods. Patient portal messaging, an electronic medical record–based method, resulted in the highest response rate (4%), screening visit completion rate (37%), and randomization rate (21%) among these eight methods. Electronic medical record–based methods ($60) were more cost-effective per response than non-electronic medical record–based methods ($107). Electronic-based methods, including patient portal messaging and Facebook, had the highest proportion of White individuals screened (52% and 60%). Direct mail to non-active patient portal increased enrollment of traditionally under-represented groups, including both women and African Americans.Conclusion:An electronic medical record–based recruitment strategy that utilized the electronic medical record for participant identification and postal mailing for participant outreach was cost-effective and increased participation of under-represented groups. This hybrid strategy represents a promising approach to improve the timely execution and broad generalizability of future clinical trials.
      Citation: Clinical Trials
      PubDate: 2020-09-16T06:14:52Z
      DOI: 10.1177/1740774520956969
       
  • Disparities in clinical trial participation and the influence of physician
           specialty
    • Authors: Jared Silberlust, Maritza M Suarez, Alberto J Caban-Martinez
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2020-09-14T07:54:22Z
      DOI: 10.1177/1740774520956578
       
  • Detecting participant noncompliance across multiple time points by
           modeling a longitudinal biomarker
    • Authors: Ross L Peterson, Joseph S Koopmeiners, Tracy T Smith, Sharon E Murphy, Eric C Donny, David M Vock
      Abstract: Clinical Trials, Ahead of Print.
      Introduction:Participant noncompliance, in which participants do not follow their assigned treatment protocol, has long complicated the interpretation of randomized clinical trials. No gold standard has been identified for detecting noncompliance, but in some trials participants’ biomarkers can provide objective information that suggests exposure to non-study treatments. However, existing methods are limited to retrospectively detecting noncompliance at a single time point based on a single biomarker measurement. We propose a novel method that can leverage participants’ full biomarker history to detect noncompliance across multiple time points. Conditional on longitudinal biomarker data, our method can estimate the probability of compliance at (1) a single time point of the trial, (2) all time points, and (3) a future time point.Methods:Across time points, we model the biomarker as a mixture density with (latent) components corresponding to longitudinal patterns of compliance. To estimate the mixture density, we fit mixed effects models for both compliance and the biomarker. We use the mixture density to derive compliance probabilities that condition on the longitudinal biomarker data. We evaluate our compliance probabilities by simulation and apply them to a trial in which current smokers were asked to only smoke low nicotine study cigarettes (Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2). In the simulation, we investigated three different effects of compliance on the biomarker, as well as the effect of misspecification of the covariance structures. We compared probability estimators (1) and (2) to those that ignore the longitudinal correlation in the data according to area under the receiver operating characteristic curve. We evaluated estimator (3) by plotting its calibration lines. For Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, we compared estimators (1) and (3) to a probability estimator of compliance at the last time point that ignores the longitudinal correlation.Results:In the simulation, for both compliance at the last time point and at all time points, conditioning on the longitudinal biomarker data uniformly raised area under the receiver operating characteristic curve across all three compliance effect scenarios. The gains in area under the receiver operating characteristic curve were smaller under misspecification. The calibration lines for the prediction of compliance closely followed 45°, though with additional variability under misspecification. For compliance at the last time point of Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, conditioning on participants’ full biomarker history boosted area under the receiver operating characteristic curve by three percentage points. The prediction probabilities somewhat accurately approximated the non-longitudinal compliance probabilities.Discussion:Compared to existing methods that only use a single biomarker measurement, our method can account for the longitudinal correlation in the biomarker and compliance to more accurately identify noncompliant participants. Our method can also use participants’ biomarker history to predict compliance at a future time point.
      Citation: Clinical Trials
      PubDate: 2020-09-14T07:53:45Z
      DOI: 10.1177/1740774520956949
       
  • Behavioral and social science research to support development of
           educational materials for clinical trials of broadly neutralizing
           antibodies for HIV treatment and prevention
    • Authors: Pablo K Valente, Yumeng Wu, Yehuda Z Cohen, Marina Caskey, Kathrine Meyers
      Abstract: Clinical Trials, Ahead of Print.
      Background/AimsEarly integration of behavioral and social sciences research into clinical trials can improve trial conduct and facilitate future implementation of biomedical interventions. We sought to examine participants’ experiences in clinical trials with broadly neutralizing antibodies and describe the development of educational materials for use in future broadly neutralizing antibody research.MethodsWe conducted semi-structured interviews with trial participants in phase 1 trials evaluating safety and efficacy of broadly neutralizing antibodies for HIV prevention and treatment and key informants (i.e. trial staff involved in broadly neutralizing antibody research). Semi-structured interviews were transcribed and analyzed thematically. Based on findings from the interviews, we developed educational materials addressing concerns and misconceptions identified among trial participants with input from community and research stakeholders. Educational materials were used in subsequent clinical trials with broadly neutralizing antibodies. We evaluated trial staff’s experiences with newly developed educational materials in follow-up key informant interviews.ResultsAlthough most participants were concerned about long-term harms related to the investigational product upon enrollment, absence of severe side effects in the trial led to an underestimation of risks related to the study during trial participation. Participants showed a poor understanding of what broadly neutralizing antibodies are and the differences between broadly neutralizing antibodies and other HIV prevention and treatment products, such as antiretrovirals. Many trial participants overestimated the possible public health impact of the broadly neutralizing antibody trials in which they were enrolled, associating broadly neutralizing antibody research with the development of vaccine or cure for HIV in the near future. Based on these concerns and misconceptions among trial participants, we developed a frequently asked questions document and adapted an existing educational video about broadly neutralizing antibodies. In follow-up interviews, key informants reported that materials helped address trial participants’ concerns and questions related to the trial. Key informants reported using the educational materials not only during informed consent but also throughout trial participation, which contributed to making informed consent an “ongoing” process.ConclusionIntegration of behavioral research into clinical trials with broadly neutralizing antibodies is key to identify and address key concerns among trial participants. Behavioral and social sciences research promotes communication between trial participants and biomedical researchers, facilitates engagement of participants and trial staff, and strengthens trial conduct. Development of educational materials collaboratively by behavioral and clinical scientists, trial staff, and community stakeholders is feasible and may help to address trial participants’ concerns and misconceptions. Future research should evaluate the impact of educational materials in recruitment and retention of trial participants.
      Citation: Clinical Trials
      PubDate: 2020-08-25T04:20:32Z
      DOI: 10.1177/1740774520948042
       
  • Empirical power comparison of statistical tests in contemporary phase III
           randomized controlled trials with time-to-event outcomes in oncology
    • Authors: Miki Horiguchi, Michael J Hassett, Hajime Uno
      First page: 597
      Abstract: Clinical Trials, Ahead of Print.
      Background:More than 95% of recent cancer randomized controlled trials used the log-rank test to detect a treatment difference making it the predominant tool for comparing two survival functions. As with other tests, the log-rank test has both advantages and disadvantages. One advantage is that it offers the highest power against proportional hazards differences, which may be a major reason why alternative methods have rarely been employed in practice. The performance of statistical tests has traditionally been investigated both theoretically and numerically for several patterns of difference between two survival functions. However, to the best of our knowledge, there has been no attempt to compare the performance of various statistical tests using empirical data from past oncology randomized controlled trials. So, it is unknown whether the log-rank test offers a meaningful power advantage over alternative testing methods in contemporary cancer randomized controlled trials. Focusing on recently reported phase III cancer randomized controlled trials, we assessed whether the log-rank test gave meaningfully greater power when compared with five alternative testing methods: generalized Wilcoxon, test based on maximum of test statistics from multiple weighted log-rank tests, difference in t-year event rate, and difference in restricted mean survival time with fixed and adaptive [math].Methods:Using manuscripts from cancer randomized controlled trials recently published in high-tier clinical journals, we reconstructed patient-level data for overall survival (69 trials) and progression-free survival (54 trials). For each trial endpoint, we estimated the empirical power of each test. Empirical power was measured as the proportion of trials for which a test would have identified a significant result (p value 
      Citation: Clinical Trials
      PubDate: 2020-09-16T06:17:09Z
      DOI: 10.1177/1740774520940256
       
  • Summarising salient information on historical controls: A structured
           assessment of validity and comparability across studies
    • Authors: Anthony Hatswell, Nick Freemantle, Gianluca Baio, Emmanuel Lesaffre, Joost van Rosmalen
      First page: 607
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundWhile placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions.MethodsWe discuss the original ‘Pocock’ criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer.ResultsA number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be.ConclusionHistorical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.
      Citation: Clinical Trials
      PubDate: 2020-09-22T05:23:34Z
      DOI: 10.1177/1740774520944855
       
  • Methodologies for pragmatic and efficient assessment of benefits and
           harms: Application to the SOCRATES trial
    • Authors: Scott R Evans, Mikael Knutsson, Pierre Amarenco, Gregory W Albers, Philip M Bath, Hans Denison, Per Ladenvall, Jenny Jonasson, J Donald Easton, Kazuo Minematsu, Carlos A Molina, Yongjun Wang, KS Lawrence Wong, S Claiborne Johnston
      First page: 617
      Abstract: Clinical Trials, Ahead of Print.
      Background/Aims:Standard approaches to trial design and analyses can be inefficient and non-pragmatic. Failure to consider a range of outcomes impedes evidence-based interpretation and reduces power. Traditional approaches synthesizing information obtained from separate analysis of each outcome fail to incorporate associations between outcomes and recognize the cumulative nature of outcomes in individual patients, suffer from competing risk complexities during interpretation, and since efficacy and safety analyses are often conducted on different populations, generalizability is unclear. Pragmatic and efficient approaches to trial design and analyses are needed.Methods:Approaches providing a pragmatic assessment of benefits and harms of interventions, summarizing outcomes experienced by patients, and providing sample size efficiencies are described. Ordinal outcomes recognize finer gradations of patient responses. Desirability of outcome ranking is an ordinal outcome combining benefits and harms within patients. Analysis of desirability of outcome ranking can be based on rank-based methodologies including the desirability of outcome ranking probability, the win ratio, and the proportion in favor of treatment. Partial credit analyses, involving grading the levels of the desirability of outcome ranking outcome similar to an academic test, provides an alternative approach. The methodologies are demonstrated using the acute stroke or transient ischemic attack treated with aspirin or ticagrelor and patient outcomes study (SOCRATES; NCT01994720), a randomized clinical trial.Results:Two 5-level ordinal outcomes were developed for SOCRATES. The first was based on a modified Rankin scale. The odds ratio is 0.86 (95% confidence interval = 0.75, 0.99; p = 0.04) indicating that the odds of worse stroke categorization for a trial participant assigned to ticagrelor is 0.86 times that of a trial participant assigned to aspirin. The 5-level desirability of outcome ranking outcome incorporated and prioritized survival; the number of strokes, myocardial infarction, and major bleeding events; and whether a stroke event was disabling. The desirability of outcome ranking probability and win ratio are 0.504 (95% confidence interval = 0.499, 0.508; p = 0.10) and 1.11 (95% confidence interval = 0.98, 1.26; p = 0.10), respectively, implying that the probability of a more desirable result with ticagrelor is 50.4% and that a more desirable result occurs 1.11 times more frequently on ticagrelor versus aspirin.Conclusion:Ordinal outcomes can improve efficiency through required pre-specification, careful construction, and analyses. Greater pragmatism can be obtained by composing outcomes within patients. Desirability of outcome ranking provides a global assessment of the benefits and harms that more closely reflect the experience of patients. The desirability of outcome ranking probability, the proportion in favor of treatment, the win ratio, and partial credit can more optimally inform patient treatment, enhance the understanding of the totality of intervention effects on patients, and potentially provide efficiencies over standard analyses. The methods provide the infrastructure for incorporating patient values and estimating personalized effects.
      Citation: Clinical Trials
      PubDate: 2020-07-15T11:53:57Z
      DOI: 10.1177/1740774520941441
       
  • Intent-to-treat analysis of cluster randomized trials when clusters report
           unidentifiable outcome proportions
    • Authors: Stacia M DeSantis, Ruosha Li, Yefei Zhang, Xueying Wang, Sally W Vernon, Barbara C Tilley, Gary Koch
      First page: 627
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundCluster randomized trials are designed to evaluate interventions at the cluster or group level. When clusters are randomized but some clusters report no or non-analyzable data, intent-to-treat analysis, the gold standard for the analysis of randomized controlled trials, can be compromised. This article presents a very flexible statistical methodology for cluster randomized trials whose outcome is a cluster-level proportion (e.g. proportion from a cluster reporting an event) in the setting where clusters report non-analyzable data (which in general could be due to nonadherence, dropout, missingness, etc.). The approach is motivated by a previously published stratified randomized controlled trial called, “The Randomized Recruitment Intervention Trial (RECRUIT),” designed to examine the effectiveness of a trust-based continuous quality improvement intervention on increasing minority recruitment into clinical trials (ClinicalTrials.gov Identifier: NCT01911208).MethodsThe novel approach exploits the use of generalized estimating equations for cluster-level reports, such that all clusters randomized at baseline are able to be analyzed, and intervention effects are presented as risk ratios. Simulation studies under different outcome missingness scenarios and a variety of intra-cluster correlations are conducted. A comparative analysis of the method with imputation and per protocol approaches for RECRUIT is presented.ResultsSimulation results show the novel approach produces unbiased and efficient estimates of the intervention effect that maintain the nominal type I error rate. Application to RECRUIT shows similar effect sizes when compared to the imputation and per protocol approach.ConclusionThe article demonstrates that an innovative bivariate generalized estimating equations framework allows one to implement an intent-to-treat analysis to obtain risk ratios or odds ratios, for a variety of cluster randomized designs.
      Citation: Clinical Trials
      PubDate: 2020-08-25T04:19:44Z
      DOI: 10.1177/1740774520936668
       
  • Retrospective collection of 90-day modified Rankin Scale is accurate
    • Authors: Mengxi Wang, Suja S Rajan, Asha P Jacob, Noopur Singh, Stephanie A Parker, Ritvij Bowry, James C Grotta, Jose-Miguel Yamal
      First page: 637
      Abstract: Clinical Trials, Ahead of Print.
      Background:The 90-day modified Rankin Scale is a widely used outcome after stroke but is sometimes hard to ascertain due to loss to follow-up. Missing outcomes can result in biased and/or inefficient estimates in clinical trials. The aim of this study is to assess the validity of acquiring the 90-day modified Rankin Scale at a later point of time when the patient has been lost at 90 days to impute the missing value.Methods:Participants who had prospectively completed a 90-day modified Rankin Scale questionnaire on their own in the Benefits of Stroke Treatment Using a Mobile Stroke Unit study were randomly interviewed to recall the 90-day modified Rankin Scale at 6, 9, or 12 months after hospital discharge over the phone. Concordance between the two scores was assessed using kappa and weighted kappa statistics. Logistic regression was used to identify factors associated with inconsistent reporting of the 90-day modified Rankin Scale.Results:Substantial agreement was observed between in-the-moment and retrospective 90-day modified Rankin Scale recalled at 6, 9, or 12 months (weighted kappa = 0.93, 95% confidence interval: 0.89–0.98; weighted kappa = 0.93, 95% confidence interval: 0.85–1.00 and weighted kappa = 0.89, 95% confidence interval: 0.82–0.95, respectively).Conclusion:Retrospective recall of 90-day modified Rankin Scale at a later time point is a valid means to impute missing data in stroke clinical trials.
      Citation: Clinical Trials
      PubDate: 2020-08-05T11:12:06Z
      DOI: 10.1177/1740774520942466
       
  • The DURATIONS randomised trial design: Estimation targets, analysis
           methods and operating characteristics
    • Authors: Matteo Quartagno, James R Carpenter, A Sarah Walker, Michelle Clements, Mahesh KB Parmar
      First page: 644
      Abstract: Clinical Trials, Ahead of Print.
      Background:Designing trials to reduce treatment duration is important in several therapeutic areas, including tuberculosis and bacterial infections. We recently proposed a new randomised trial design to overcome some of the limitations of standard two-arm non-inferiority trials. This DURATIONS design involves randomising patients to a number of duration arms and modelling the so-called ‘duration-response curve’. This article investigates the operating characteristics (type-1 and type-2 errors) of different statistical methods of drawing inference from the estimated curve.Methods:Our first estimation target is the shortest duration non-inferior to the control (maximum) duration within a specific risk difference margin. We compare different methods of estimating this quantity, including using model confidence bands, the delta method and bootstrap. We then explore the generalisability of results to estimation targets which focus on absolute event rates, risk ratio and gradient of the curve.Results:We show through simulations that, in most scenarios and for most of the estimation targets, using the bootstrap to estimate variability around the target duration leads to good results for DURATIONS design-appropriate quantities analogous to power and type-1 error. Using model confidence bands is not recommended, while the delta method leads to inflated type-1 error in some scenarios, particularly when the optimal duration is very close to one of the randomised durations.Conclusions:Using the bootstrap to estimate the optimal duration in a DURATIONS design has good operating characteristics in a wide range of scenarios and can be used with confidence by researchers wishing to design a DURATIONS trial to reduce treatment duration. Uncertainty around several different targets can be estimated with this bootstrap approach.
      Citation: Clinical Trials
      PubDate: 2020-08-17T06:19:48Z
      DOI: 10.1177/1740774520944377
       
  • Exploring mechanisms of action in clinical trials of complex surgical
           interventions using mediation analysis
    • Authors: Linda Sharples, Olympia Papachristofi, Saleema Rex, Sabine Landau
      First page: 654
      Abstract: Clinical Trials, Ahead of Print.
      Background:Surgical interventions allow for tailoring of treatment to individual patients and implementation may vary with surgeon and healthcare provider. In addition, in clinical trials assessing two competing surgical interventions, the treatments may be accompanied by co-interventions.Aims:This study explores the use of causal mediation analysis to (1) delineate the treatment effect that results directly from the surgical intervention under study and the indirect effect acting through a co-intervention and (2) to evaluate the benefit of the surgical intervention if either everybody in the trial population received the co-intervention or nobody received it.Methods:Within a counterfactual framework, relevant direct and indirect effects of a surgical intervention are estimated and adjusted for confounding via parametric regression models, for the situation where both mediator and outcome are binary, with baseline stratification factors included as fixed effects and surgeons as random intercepts. The causal difference in probability of a successful outcome (estimand of interest) is calculated using Monte Carlo simulation with bootstrapping for confidence intervals. Packages for estimation within standard statistical software are reviewed briefly. A step by step application of methods is illustrated using the Amaze randomised trial of ablation as an adjunct to cardiac surgery in patients with irregular heart rhythm, with a co-intervention (removal of the left atrial appendage) administered to a subset of participants at the surgeon’s discretion. The primary outcome was return to normal heart rhythm at one year post surgery.Results:In Amaze, 17% (95% confidence interval: 6%, 28%) more patients in the active arm had a successful outcome, but there was a large difference between active and control arms in the proportion of patients who received the co-intervention (55% and 30%, respectively). Causal mediation analysis suggested that around 1% of the treatment effect was attributable to the co-intervention (16% natural direct effect). The controlled direct effect ranged from 18% (6%, 30%) if the co-intervention were mandated, to 14% (2%, 25%) if it were prohibited. Including age as a moderator of the mediation effects showed that the natural direct effect of ablation appeared to decrease with age.Conclusions:Causal mediation analysis is a useful quantitative tool to explore mediating effects of co-interventions in surgical trials. In Amaze, investigators could be reassured that the effect of the active treatment, not explainable by differential use of the co-intervention, was significant across analyses.
      Citation: Clinical Trials
      PubDate: 2020-08-20T10:22:51Z
      DOI: 10.1177/1740774520947644
       
  • Effectiveness of social media (Facebook), targeted mailing, and in-person
           solicitation for the recruitment of young adult in a diabetes
           self-management clinical trial
    • Authors: Sarah-Jeanne Salvy, Kristine Carandang, Cheryl LP Vigen, Alyssa Concha-Chavez, Paola A Sequeira, Jeanine Blanchard, Jesus Diaz, Jennifer Raymond, Elizabeth A Pyatak
      First page: 664
      Abstract: Clinical Trials, Ahead of Print.
      Background/AimsResearch is needed to identify promising recruitment strategies to reach and engage diverse young adults in diabetes clinical research. The aim of this study was to examine the relative strengths and weaknesses of three recruitment strategies used in a diabetes self-management clinical trial: social media advertising (Facebook), targeted mailing, and in-person solicitation of clinic patients.MethodsStrategies were compared in terms of (1) cost-effectiveness (i.e. cost of recruitment/number of enrolled participants), (2) ability to yield participants who would not otherwise be reached by alternative strategies, and (3) likelihood of participants recruited through each strategy to adhere to study procedures. We further explored the appeal (overall and among age and gender subgroups) of social media advertisement features.ResultsIn-person recruitment of clinic patients was overall the most cost-effective strategy. However, differences in demographic, clinical, and psychosocial characteristics of participants recruited via different strategies suggest that the combination of these approaches yielded a more diverse sample than would any one strategy alone. Once successfully enrolled, there was no difference in study completion and intervention adherence between individuals recruited by the three recruitment strategies.ConclusionsUltimately, the utility of a recruitment strategy is defined by its ability to effectively attract people representative of the target population who are willing to enroll in and complete the study. Leveraging a variety of recruitment strategies appears to produce a more representative sample of young adults, including those who are less engaged in diabetes care.
      Citation: Clinical Trials
      PubDate: 2020-07-06T09:48:13Z
      DOI: 10.1177/1740774520933362
       
  • Recruiting breast cancer patients for mHealth research: Obstacles to
           clinic-based recruitment for a mobile phone app intervention study
    • Authors: Alisha Gupta, Gabrielle Ocker, Philip I Chow
      First page: 675
      Abstract: Clinical Trials, Ahead of Print.
      BackgroundNearly half of newly diagnosed breast cancer patients will report clinically significant symptoms of depression and/or anxiety within the first year of diagnosis. Research on the trajectory of distress in cancer patients suggests that targeting patients early in the diagnostic pathway could be particularly impactful. Given the recent rise of smartphone adoption, apps are a convenient and accessible platform from which to deliver mental health support; however, little research has examined their potential impact among newly diagnosed cancer patients. One reason is likely due to the obstacles associated with in-clinic recruitment of newly diagnosed cancer patients for mHealth pilot studies.MethodsThis article draws from our experiences of a recently completed pilot study to test a suite of mental health apps in newly diagnosed breast cancer patients. Recruitment strategies included in-clinic pamphlets, flyers, and direct communication with clinicians. Surgical oncologists and research staff members approached eligible patients after a medical appointment. Research team members met with patients to provide informed consent and review the study schedule.ResultsFour domains of in-clinic recruitment challenges emerged: (a) coordination with clinic staff, (b) perceived burden among breast cancer patients, (c) limitations regarding the adoption and use of technology, and (d) availability of resources. Potential solutions are provided for each challenge.ConclusionRecruitment of newly diagnosed cancer patients is a major challenge to conducting mobile intervention studies for researchers on a pilot-study budget. To realize the impact of mobile interventions for the most vulnerable cancer patient populations, health researchers must address barriers to in-clinic recruitment to provide vital preliminary data in proposals of large-scale research projects.
      Citation: Clinical Trials
      PubDate: 2020-07-14T08:41:04Z
      DOI: 10.1177/1740774520939247
       
  • Recruitment, retention, and adherence in a clinical trial: The Pediatric
           Heart Network’s Marfan Trial experience
    • Authors: Michelle S Hamstra, Victoria L Pemberton, Nicholas Dagincourt, Danielle Hollenbeck-Pringle, Felicia L Trachtenberg, James F Cnota, Andrew M Atz, Elizabeth Cappella, Sylvia De Nobele, Josephine Grima, Martha King, Rosalind Korsin, Linda M Lambert, Meghan K MacNeal, Larry W Markham, Gretchen MacCarrick, Donna M Sylvester, Patricia Walter, Mingfen Xu, Ronald V Lacro
      First page: 684
      Abstract: Clinical Trials, Ahead of Print.
      Background/Aims:The Pediatric Heart Network Marfan Trial was a randomized trial comparing atenolol versus losartan on aortic root dilation in 608 children and young adults with Marfan syndrome. Barriers to enrollment included a limited pool of eligible participants, restrictive entry criteria, and a diverse age range that required pediatric and adult expertise. Retention was complicated by a 3-year commitment to a complex study and medication regimen. The Network partnered with the Marfan Foundation, bridging the community with the research. The aims of this study are to report protocol and medication adherence and associated predictive factors, and to describe recruitment and retention strategies.Methods:Recruitment, retention, and adherence to protocol activities related to the primary outcome were measured. Retention was measured by percentage of enrolled participants with 3-year outcome data. Protocol adherence was calculated by completion rates of study visits, ambulatory electrocardiography (Holter monitoring), and quarterly calls. Medication adherence was assessed by the number of tablets or the amount of liquid in bottles returned. Centers were ranked according to adherence (high, medium, and low tertiles). Recruitment, retention, and adherence questionnaires were completed by sites. Descriptive statistics summarized recruitment, retention, and adherence, as well as questionnaire results. Regression modeling assessed predictors of adherence.Results:Completion rates for visits, Holter monitors, and quarterly calls were 99%, 94%, and 96%, respectively. Primary outcome data at 3 years were obtained for 88% of participants. The mean percentage of medication taken was estimated at 89%. Site and age were associated with all measures of adherence. Young adult and African American participants had lower levels of adherence. Higher adherence sites employed more strategies; had more staffing resources, less key staff turnover, and more collaboration with referring providers; utilized the Foundation’s resources; and used a greater number of strategies to recruit, retain, and promote protocol and medication adherence.Conclusion:Overall adherence was excellent for this trial conducted within a National Institutes of Health–funded clinical trial network. Strategies specifically targeted to young adults and African Americans may have been beneficial. Many strategies employed by higher adherence sites are ones that any site could easily use, such as greeting families at non-study hospital visits, asking for family feedback, providing calendars for tracking schedules, and recommending apps for medication reminders. Additional key learnings include adherence differences by age, race, and site, the value of collaborative learning, and the importance of partnerships with patient advocacy groups. These lessons could shape recruitment, retention, and adherence to improve the quality of future complex trials involving rare conditions.
      Citation: Clinical Trials
      PubDate: 2020-08-21T12:00:12Z
      DOI: 10.1177/1740774520945988
       
  • When and how to include vulnerable subjects in clinical trials
    • Authors: David Wendler
      First page: 696
      Abstract: Clinical Trials, Ahead of Print.
      There has been a good deal of discussion in the literature regarding which subjects are vulnerable in the context of clinical trials. There has been significantly less discussion regarding when and how to include vulnerable subjects in clinical trials. This lack of guidance is a particular problem for trials covered by the US regulations, which mandate strict requirements on the inclusion of three groups: pregnant women/fetuses, prisoners, and children. For the past 30 years, funders, investigators, and institutional review boards have frequently responded to these regulations by excluding pregnant women/fetuses, prisoners, and children from clinical trials. More recent work has emphasized the extent to which a default of exclusion can undermine the value of clinical trials, especially pragmatic trials. A default of exclusion also has the potential to undermine the interests of vulnerable groups, in both the short and the long term. These concerns raise the need for guidance on how to satisfy existing US regulations, while minimizing their negative impact on the value of clinical trials and the interests of vulnerable groups. The present manuscript thus describes a six-step decision procedure that institutional review boards can use to determine when and how to include vulnerable subjects in clinical trials, including pragmatic trials, that are covered by US regulations.
      Citation: Clinical Trials
      PubDate: 2020-08-18T05:05:28Z
      DOI: 10.1177/1740774520945601
       
  • User-centered design principles in the development of clinical research
           tools
    • Authors: Anita Walden, Lynsi Garvin, Michelle Smerek, Constance Johnson
      First page: 703
      Abstract: Clinical Trials, Ahead of Print.
      Background:Increasing and sustaining the engagement of participants in clinical research studies is a goal for clinical investigators, especially for studies that require long-term or frequent involvement of participants. Technology can be used to reduce barriers to participation by providing multiple options for clinical data entry and form submission. However, electronic systems used in clinical research studies should be user-friendly while also ensuring data quality. Directly involving study participants in evaluating the effectiveness and usability of electronic tools may promote wider adoption, maintain involvement, and increase user satisfaction of the technology. While developers of healthcare applications have incorporated user-centered designs, these methods remain uncommon in the design of clinical study tools such as patient-reported outcome surveys or electronic data capture digital health tools.Methods:Our study evaluated whether the clinical research setting may benefit from implementing user-centered design principles. Study participants were recruited to test the web-based form for the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study Community Translational Population Health Registry and Biorepository that would enable them to complete their study forms electronically. The study enrollment form collects disease history, conditions, smoking status, medications, and other information. The system was initially evaluated by the data management team through traditional user-acceptance testing methods. During the tool evaluation phase, a decision was made to incorporate a small-scale usability study to directly test the system.Results:Results showed that a majority of participants found the system easy to use. Of the eight required tasks, 75% were completed successfully. Of the 72 heuristics violated, language was the most frequent violation.Conclusion:Our study showed that user-centered usability methods can identify important issues and capture information that can enhance the participant’s experience and may improve the quality of study tools.
      Citation: Clinical Trials
      PubDate: 2020-08-20T10:22:51Z
      DOI: 10.1177/1740774520946314
       
  • Barriers to uptake of the hip fracture core outcome set: An international
           survey of 80 hip fracture trialists
    • Authors: Jessica Fletcher, Katie Jane Sheehan, Toby O Smith
      First page: 712
      Abstract: Clinical Trials, Ahead of Print.
      Background:Core outcome sets are an agreed recommendation to inform the selection of outcome measures in clinical trials. There has been low uptake of the 2014 hip fracture core outcome set. The reasons for this remain unclear. The aim of this study was to understand the reasons for the non-adoption and approaches to increase adoption of the hip fracture core outcome set.Methods:Randomised controlled trials from PubMed (2017–2019) and ClinicalTrials.gov (2015–2019) were identified. Corresponding authors for each identified trial (n = 302) were surveyed using five questions on awareness of the hip fracture core outcome set, reasons for non-adoption and approaches to increase adoption. Data were analysed descriptively using frequencies, mean values and standard deviations.Results:Fifty-four percent of the respondents (n = 43) were aware of the concept of core outcome set. Only 15% (n = 12) based the outcome measure selection on the 2014 hip fracture core outcome set. Key reasons for non-adoption included the following: authors being unaware and perceived inappropriateness to their trial design. Eighty-six percent (n = 69) of respondents agreed to the need for increased awareness of core outcome sets through research training, academic and clinical journal requirements, and funding or publication stipulations. Eighty-eight percent (n = 70) of respondents indicated the current core outcome set required revision to focus on trials investigating people with cognitive impairment, caregivers, rehabilitation, surgical interventions and anaesthetic trial designs.Conclusion:Barriers to the adoption of the hip fracture core outcome set centre on education, awareness of the core outcome sets and applicability to the breath of hip fracture trial designs. Further consideration should be made to address these, to improve the harmonisation of outcome measures across hip fracture trials.
      Citation: Clinical Trials
      PubDate: 2020-07-17T06:46:51Z
      DOI: 10.1177/1740774520941444
       
  • Addressing the quality of submissions to ClinicalTrials.gov for
           registration and results posting: The use of a checklist
    • Authors: Oswald Tetteh, Prince Nuamah, Anthony Keyes
      First page: 717
      Abstract: Clinical Trials, Ahead of Print.
      Background:US Federal regulations since the late 1990s have required registration of some clinical trials and submission of results for some of these trials on a public registry, ClinicalTrials.gov. The quality of the submissions made to ClinicalTrials.gov determines the duration of the Quality Control review, whether the submission will pass the review (success), and how many review cycles it will take for a study to be posted. Success rate for all results submitted to ClinicalTrials.gov is less than 25%. To increase the success of investigators’ submissions and meet the requirements of registration and submission of results in a timely fashion, the Johns Hopkins ClinicalTrials.gov Program implemented a policy to review all studies for quality before submission. To standardize our review for quality, minimize inter-reviewer variability, and have a tool for training new staff, we developed a checklist.Methods:The Program staff learned from major comments received from ClinicalTrials.gov and also reviewed the Protocol Registration and Results System review criteria for registration and results to fully understand how to prepare studies to pass Quality Control review. These were summarized into bulleted points and incorporated into a checklist used by Program staff to review studies before submission.Results:In the period before the introduction of the checklist, 107 studies were submitted for registration with a 45% (48/107) success rate, a mean (SD) of 18.9 (26.72) days in review, and 1.74 (0.78) submission cycles. Results for 44 records were submitted with 11% (5/44) success rate, 115.80 (129.33) days in review, and 2.23 (0.68) submission cycles. In the period after the checklist, 104 studies were submitted for registration with 80% (83/104) success rate, 2.12 (3.85) days in review, and 1.22 (0.46) submission cycles. Results for 22 records were submitted with 41% (9/22) success rate, 39.27 (19.84) days in review, and 1.64 (0.58) submission cycles. Of the 44 results submitted prior to the checklist, 30 were Applicable or Probable Applicable Clinical Trials, with 10% (3/30) being posted within 30 days as required of the National Institutes of Health. For the 22 results submitted after the checklist, 17 were Applicable or Probable Applicable Clinical Trials, with 47% (8/17) being posted within 30 days of submission. These pre- and post-checklist differences were statistically significant improvements.Conclusion:The checklist has substantially improved our success rate and contributed to a reduction in the review days and number of review cycles. If Academic Medical Centers and industry will adopt or create a similar checklist to review their studies before submission, the quality of the submissions can be improved and the duration of review can be minimized.
      Citation: Clinical Trials
      PubDate: 2020-08-05T11:13:45Z
      DOI: 10.1177/1740774520942746
       
  • Non-inferiority trials using a surrogate marker as the primary endpoint:
           An increasing phenotype in cardiovascular trials
    • Authors: Behnood Bikdeli, César Caraballo, John Welsh, Joseph S Ross, Sanjay Kaul, Gregg W Stone, Harlan M Krumholz
      First page: 723
      Abstract: Clinical Trials, Ahead of Print.
      Background/aimsNon-inferiority trials are increasing in cardiovascular medicine, with approval of many drugs and devices on the basis of such studies. Surrogate markers as primary endpoints have been also more frequently used for efficient assessment of cardiovascular interventions. However, there is uncertainty about their concordance with clinical outcomes. Non-inferiority design using a surrogate marker as a primary endpoint may pose particular challenges in clinical interpretation. We sought to explore the publication trends, methodology, and reporting features of non-inferiority cardiovascular trials that used a primary surrogate marker as the primary endpoint.MethodsWe searched six high-impact journals (The New England Journal of Medicine, The Journal of the American Medical Association, The Lancet, The Journal of the American College of Cardiology, Circulation, and European Heart Journal) from 1 January 1990 to 31 December 2018 and identified non-inferiority cardiovascular trials that used a surrogate marker as the primary endpoint. We assessed the non-inferiority margin reported in the manuscript and other publicly available platforms (e.g. protocol, clinicaltrials.gov). We also determined whether the included non-inferiority trials with surrogate markers as primary endpoints were followed by clinical outcome trials.ResultsWe screened 15,553 publications and identified 247 cardiovascular trials that used a non-inferiority design. Of these, 37 had a surrogate marker as a primary endpoint (18 drug trials, 13 device trials, 6 others). All of these non-inferiority trials with surrogate outcomes were published after 2000, mostly in cardiology journals (13 in The Journal of the American College of Cardiology, 9 in European Heart Journal, 8 in Circulation, 6 in The Lancet, 1 in The New England Journal of Medicine), and their publication rate increased over time (p 
      Citation: Clinical Trials
      PubDate: 2020-08-25T04:20:02Z
      DOI: 10.1177/1740774520949157
       
  • Formulary restrictions may impact enrollment in pragmatic trials and limit
           generalizability of findings to vulnerable populations
    • Authors: Jodi B Segal
      First page: 729
      Abstract: Clinical Trials, Ahead of Print.

      Citation: Clinical Trials
      PubDate: 2020-07-11T06:00:30Z
      DOI: 10.1177/1740774520941257
       
 
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