Relevant bibliographies by topics / Clinical trials

Academic literature on the topic 'Clinical trials'

Author: Grafiati
Published: 4 June 2021
Last updated: 22 June 2024

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Journal articles on the topic "Clinical trials"

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Santhosh R Divya, Samuel. "In Silico Clinical Trials - Review." International Journal of Science and Research (IJSR) 12, no. 6 (June 5, 2023): 262–66. http://dx.doi.org/10.21275/sr23512133636.

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Vulto, Arnold G. "Clinical trials on trial." European Journal of Hospital Pharmacy 19, no. 4 (August 2012): 347. http://dx.doi.org/10.1136/ejhpharm-2012-000180.

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Dmitrienko, Alex, Gautier Paux, and Thomas Brechenmacher. "POWER CALCULATIONS IN CLINICAL TRIALS WITH COMPLEX CLINICAL OBJECTIVES." Journal of the Japanese Society of Computational Statistics 28, no. 1 (2015): 15–50. http://dx.doi.org/10.5183/jjscs.1411001_213.

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Seow, Hsien-Yeang, Patrick Whelan, Mark N. Levine, Kathryn Cowan, Barbara Lysakowski, Brenda Kowaleski, Anne Snider, Rebecca Y. Xu, and Andrew Arnold. "Funding Oncology Clinical Trials: Are Cooperative Group Trials Sustainable?" Journal of Clinical Oncology 30, no. 13 (May 1, 2012): 1456–61. http://dx.doi.org/10.1200/jco.2011.37.2698.

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Purpose Many oncology clinical trials departments (CTDs) are in serious fiscal deficit and their sustainability is in jeopardy. This study investigates whether the payment models used to fund industry versus cooperative group trials contribute to the fiscal deficit of a CTD. Methods We examined the lifetime costs of all cooperative group and industry trials activated in the CTD of a cancer center between 2007 and 2011. A trial's lifetime is defined as being from the date the first patient was accrued until the last patient's actual or projected final follow-up visit. For each trial, we calculated the lifetime monthly net income, which was defined as monthly revenue minus monthly costs. Data sources included study protocols, trial budgets, and accrual data. Results Of the 97 trials analyzed, 64 (66%) were cooperative group trials. The pattern of lifetime net income for cooperative group trials has a positive peak during patient accrual followed by a negative trough during follow-up. In contrast, the pattern for industry trials resembled an “l” shape. The patterns reflect the differing payment models: upfront lump-sum payments (cooperative group) versus milestone payments (industry). Conclusion The negative trough in the lifetime net income of a cooperative group trial occurs because follow-up costs are typically not funded or are underfunded. CTDs accrue more patients in new trials to offset that deficit. The CTD uses revenue from accrual to existing trials to cross-subsidize past trials in follow-up. As the number of patients on follow-up increases, the fiscal deficit grows larger each year, perpetuating the cycle.
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Hamm, Caroline M., Krista Naccarato, Stephen Sundquist, Suzana Kovacevic, Youshaa El-Abed, and Janet Dancey. "Clinical trials navigator: Patient-centered access to clinical trials." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e14024-e14024. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14024.

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e14024 Background: Despite recommendations from premier institutions such as NCCN that all cancer patients should be entered on clinical trials, only 3 – 5 % of adult cancer patients are enrolled on clinical trials in North America. The reason for this is multi-factorial and includes poor trial design, inappropriate endpoints, inappropriate inclusion/exclusion factors, attitudes about trial participation held by patient and/or treating physician and lack of trial availability. Methods: To address issues of trial availability, in March 2019, we initiated a novel service to help Canadian patients find clinical trial options. The service compares patient demographic and health status information provided against potential opportunities sourced using clinicaltrials.gov and Canadian clinical trials websites. A report presenting outcomes of CTN review is developed for the requesting patient or physician. An interview is provided for the self-referring patient by supporting physicians. Results: To date 96 patients have used this service. Most (94%) were stage IV or refractory/ relapsed. Smaller disease sites represented 23% of our patient population (brain, sarcoma, pancreas). Our turn-around-time from request of services to delivery of report to patient or physician improved over time and is currently 24 hours during the working week. Of those eligible, 25% of patients died before referral, the median time from referral to the CTN to the patient’s death was 109 days (3 – 188 days). Conclusions: Significant interest from both physicians and patients for this service was identified. Strategies are being developed to encourage earlier referrals to clinical trials would improve number of patients entering clinical trials as 25% of our patients. [Table: see text]
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Harman, Chloë. "Putting clinical trials on trial." Nature Reviews Nephrology 5, no. 6 (June 2009): 301. http://dx.doi.org/10.1038/nrneph.2009.89.

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Levine, Deborah J., and Fernando Torres. "Clinical Trials Update." Advances in Pulmonary Hypertension 12, no. 2 (January 1, 2013): 53–54. http://dx.doi.org/10.21693/1933-088x-12.2.53.

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The Clinical Trials Update highlights new and ongoing research trials that are evaluating therapies for PAH. In this issue, Deborah Levine, MD, examines the PATENT-1 study results, findings from CHEST-1, and outcomes of the SERAPHIN trial.
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Gnanasambandam, Anjali. "Artificial Intelligence in Clinical Trials- Future Prospectives." Bioequivalence & Bioavailability International Journal 7, no. 1 (January 4, 2023): 1–8. http://dx.doi.org/10.23880/beba-16000196.

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Clinical trials are essential for delivering novel medications, technology, and procedures to the market and clinical practice. Only 10% of these studies complete the entire procedure from the drug design to the four phases of development, because clinical trials are becoming more expensive and difficult to perform. The population's health, standard treatment, health economics, and sustainability suffered greatly from this low completion rate. Artificial intelligence (AI) is one of the tools that could streamline some of the processes which are the most tedious operations, like patient selection, matching, and enrollment; better patient selection could also minimize harmful treatment and its side effects. The widespread implementation of AI technology in clinical trials still faces many challenges and requires more high-quality prospective clinical validation. In this review, we discussed the prospective applications of AI in clinical research and patient care in the future
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Bousquet, Philippe Jean, Pascal Demoly, Giovanni Passalacqua, G. Walter Canonica, and Jean Bousquet. "Immunotherapy: clinical trials – optimal trial and clinical outcomes." Current Opinion in Allergy and Clinical Immunology 7, no. 6 (December 2007): 561–66. http://dx.doi.org/10.1097/aci.0b013e3282f1d6a4.

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Singh, Jerome Amir. "Adaptive clinical trials in public health emergency contexts: ethics considerations." Wellcome Open Research 8 (March 23, 2023): 130. http://dx.doi.org/10.12688/wellcomeopenres.19057.1.

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While Adaptive Clinical Trials (ACTs) have grown in prevalence, prominence, and impact, the ethical issues implicit in such trial designs, particularly in the context of public health emergencies, have been afforded relatively scant attention. This work argues that the ethical dimensions of ACTs should be considered at trial conception, factored into the trial’s design, and subject to ongoing evaluation during the trial’s conduct.
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Dissertations / Theses on the topic "Clinical trials"

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Kim, Anne(Anne Y. ). "Optimizing clinical trials with Open Trial Chain." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/121787.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-64).
The objective of this thesis is to study the challenges of data sharing in healthcare (namely clinical trials), and propose the use of Open Algorithms (OPAL) as a viable solution for research collaboration that allows for access to data without compromising data ownership (data is only used once for the intended purpose, raw data is never leaked, the value generated from the data is transferred to the owner). This thesis surveys the challenges unique to clinical trials, and highlights the various methods for privacy-preserving computation prior to this work. Through the overview of OPAL's solution in the space of privacy-preserving computation, we show the implementation details of how OPAL was applied to clinical trials in a project called Open Trial Chain, a platform for clinical trial data built for analytics, security, and incentivized sharing through technologies like federated learning and blockchain. With motivated examples derived from real-world reported problems in healthcare, we also demonstrate speed, accuracy, and security metrics. In the application, Open Trial Chain can drastically reduce clinical trial costs, reduce error, and increase quality of analysis diversity. Overall, this project shows promise for further extension in other health datasets for compliance in an ever-complicated move towards regulations that reflect for conscientiousness for data security, ownership, and provenance.
by Anne Kim.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Vickers, Andrew J. "Homoeopathy and clinical trials." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302395.

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Zhang, Yifan. "Bayesian Adaptive Clinical Trials." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:13070079.

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Bayesian adaptive designs are emerging as popular approach to develop adaptive clinical trials. In this dissertation, I describe the mathematical steps for computing the theoretical optimal adaptive designs in biomarker-integrated trials and in trials with survival outcomes. Section 1 discusses the optimal design in personalized medicine. The optimal design maximizes the expected trial utility given any pre-specified utility function, though the discussion here focuses on maximizing responses within a given patient horizon. This work provides absolute benchmark for the evaluation of trial designs in targeted therapy with binary treatment outcomes. While treatment efficacy can be measured by a short-term binary outcome in many phase II and phase III trials, patients' progression-free survival time is with significant importance in cancer clinical trials. However, it is often difficult to make a design adaptive to survival outcomes because of the long observation time. In Section 2, an optimal adaptive design is developed so that treatment assignment decision for later patients can be made with complete or partial survival outcomes of early patients. The design also maximizes the expected trial utility given any pre-specified utility function that is of clinical importance. In this section, the focus is on maximizing the expected progression-free survival time. Both Sections1 and 2 include examples of comparing adaptive designs, such as the bayesian adaptive randomization and the play-the-winner rule, in terms of the expected trial utility with respect to the best achievable result. In Section 3, a simulation-based p-value is proposed and can be used to conduct frequentist analysis of Bayesian adaptive clinical trials. The optimal Bayesian design is compared to the equal randomization design in terms of the Type I error and the statistical power. With a fixed trial size and Type I error, the power of the equal randomization design depends on the difference in treatment efficacy, meanwhile the power of the optimal Bayesian design also depends on the size of the patient horizon.
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Scarale, M. G. "RESPONSE - ADAPTIVE CLINICAL TRIALS." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/344736.

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The question we posed at the beginning of this thesis was whether, in the presence of a clinical superiority of one of two possible treatments, it was possible to find an appropriate statistical methodology that would allow us to reach this goal. We were thus led to explore many possibilities to carry out this analysis and randomly assign patients to the two treatments, as required by the particular nature of these experiments. Specifically, we made a close examination of the methods of randomization, especially appreciating the flexibility of the adaptive responses, and could see the strengths of urn models. We started with the study of the urn for excellence, Polya's urn. Next, we analyzed some extensions and generalizations, focusing especially on two kinds of urns with random reinforcement. We exposed the results obtained throughout simulations concerning the convergence of the proportion of the best treatment, which came from the comparison of the models studied. In the end, we showed how the urn model works in a real case, comparing two treatments with continuous response in one ICU trial on Melatonin. We'll see how the properties demonstrated in theory are confirmed in practice. The project ends by giving a hint of a new adaptive model that we have started to idealize in collaboration with the team of Prof. Parmigiani and Prof. Trippa of the "Biostatistics and Computational Biology" Department, Harvard T.H. Chan School of Public Health.
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Lui, Wai-fan. "Clinical trials research methodology current practice in Hong Kong and training programme development /." Click to view the E-thesis via HKUTO, 1998. http://sunzi.lib.hku.hk/HKUTO/record/B38628582.

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Čekanauskaitė, Asta. "Informedness about clinical trials of patients participating in placebo-controlled clinical trials in Lithuania." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20130114_081952-77101.

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The dissertation aims to analyse the problem of informed consent in clinical drug trials. Understanding of the provided information is one of the basic components of a person’s sound and informed decision to participate in a clinical trial. Our research evaluated informedness about clinical trials of patients participating in clinical drug trials in Lithuania, with emphasis on the informedness about key elements of clinical trial design (placebo-control, double-blindness, and randomisation). An anonymous survey of patients participating in clinical drug trials was conducted for the purposes of this study. The results of the study reveal that the legal framework sets the basis for adequate informedness about clinical trials of clinical trial participants, however, patients participating in placebo-controlled clinical trials are insufficiently informed about clinical trials. Patients participating in placebo-controlled clinical trials are better informed about the rights of clinical trial participants than about clinical trial design, however, informedness about design is a more important condition for overall informedness. The majority of placebo-controlled clinical trial participants do not understand at least one of the three key elements of clinical trials design and they tend to interpret the scientific methods used in clinical trials therapeutically.
Disertacijoje analizuojama informuoto asmens sutikimo įgyvendinimo klinikiniuose vaistinio preparato tyrimuose problema. Pateiktos informacijos supratimas yra viena svarbiausių asmens apsisprendimo dalyvauti klinikiniame tyrime sąlygų. Moksliname darbe vertinamas pacientų, dalyvaujančių placebu kontroliuojamuose klinikiniuose vaistinio preparato tyrimuose Lietuvoje (toliau – klinikinių tyrimų dalyviai), informuotumas apie klinikinius tyrimus, akcentuojant informuotumą apie klinikiniuose tyrimuose taikomus mokslinius metodus (placebo kontrolę, dvigubą aklumą, atsitiktinį tiriamųjų grupių sudarymą). Klinikinių tyrimų dalyvių informuotumas buvo tiriamas atliekant anoniminę apklausą. Mokslinio darbo rezultatai parodė, kad teisinis reglamentavimas sudaro prielaidas klinikinių tyrimų dalyvių informuotumui, tačiau jų informuotumas yra nepakankamas. Respondentai buvo geriau informuoti apie klinikinių tyrimų dalyvių teises nei apie klinikinių tyrimų metodologiją, tačiau informuotumas apie klinikinių tyrimų metodologiją yra svarbesnė prielaida bendram informuotumui. Dauguma klinikinių tyrimų dalyvių nesuprato vieno ar daugiau iš trijų pagrindinių klinikiniuose tyrimuose taikomų mokslinių metodų (placebo kontrolės, dvigubo aklumo, atsitiktinio tiriamųjų grupių sudarymo) ir buvo linkę suteikti jiems terapinę reikšmę. Tyrimo rezultatai sudaro prielaidas kryptingam klinikinių tyrimų dalyvių informuotumo gerinimui, informacijos apie klinikinius tyrimus viešinimui.
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Kehl, Victoria. "Responder Identification in Clinical Trials." Diss., lmu, 2002. http://nbn-resolving.de/urn:nbn:de:bvb:19-5908.

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Sydow, Victoria. "Clinical Trials - Competitive resource management." Thesis, KTH, Skolan för bioteknologi (BIO), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-149476.

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Murphy, Jeremy James. "Clinical trials in cerebrovascular disease." Thesis, University of Nottingham, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293328.

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Machin, David. "Statistical aspects of clinical trials." Thesis, University of Southampton, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.257667.

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Books on the topic "Clinical trials"

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Piantadosi, Steven. Clinical Trials. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471740136.

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Pocock, Stuart J. Clinical Trials. West Sussex, England: John Wiley & Sons Ltd,., 2013. http://dx.doi.org/10.1002/9781118793916.

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Pawlik, Timothy M., and Julie A. Sosa, eds. Clinical Trials. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35488-6.

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Lelia, Duley, and Farrell Barbara 1946-, eds. Clinical trials. London: BMJ Books, 2002.

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Rabinarayan, Dash. Clinical trials. New Delhi: Studera Press, 2017.

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Mantel, Barbara. Clinical Trials. 2455 Teller Road, Thousand Oaks California 91320 United States: CQ Press, 2018. http://dx.doi.org/10.4135/cqresrre20180518.

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Kelly, William Kevin, and Susan Halabi, eds. Oncology Clinical Trials. New York, NY: Springer Publishing Company, 2018. http://dx.doi.org/10.1891/9780826168733.

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Anthony, Riley W., and Murray Patricia J, eds. Conducting clinical trials. New York: Plenum Medical Book Co., 1987.

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Kerr, David J., Kirstine Knox, Diane C. Robertson, Derek Stewart, and Rene Watson, eds. Clinical Trials Explained. Malden, Massachusetts, USA: Blackwell Publishing Ltd, 2006. http://dx.doi.org/10.1002/9780470750612.

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Machin, David, and Peter M. Fayers. Randomized Clinical Trials. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470686232.

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Book chapters on the topic "Clinical trials"

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Patra, Jayanta Kumar, Swagat Kumar Das, Gitishree Das, and Hrudayanath Thatoi. "Clinical Trials." In A Practical Guide to Pharmacological Biotechnology, 109–26. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6355-9_6.

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Basu, Bikramjit, and Sourabh Ghosh. "Clinical Trials." In Biomaterials for Musculoskeletal Regeneration, 191–216. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3017-8_9.

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McKneally, M. F., B. McPeek, F. Mosteller, and E. A. M. Neugebauer. "Clinical Trials." In Surgical Research, 197–209. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1888-3_26.

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Rhoads, Kim F., and Julio E. Garcia-Aguilar. "Clinical Trials." In Transanal Endoscopic Microsurgery, 125–33. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76397-2_13.

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Staquet, M. "Clinical Trials." In Surgical Oncology, 446–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-72646-0_43.

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Chen, Donna T. "Clinical Trials." In Encyclopedia of Women’s Health, 271–73. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-0-306-48113-0_91.

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Hay, Annette E., Lorna Fern, Ralph M. Meyer, Nita Seibel, and Ronald Barr. "Clinical Trials." In Cancer in Adolescents and Young Adults, 549–63. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33679-4_21.

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Rudge, Peter. "Clinical Trials." In Multiple Sclerosis Research in Europe, 13–14. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4143-4_2.

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Cohen, Margo Panush. "Clinical Trials." In The Polyol Paradigm and Complications of Diabetes, 105–12. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4670-1_7.

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Zanders, Edward D. "Clinical Trials." In The Science and Business of Drug Discovery, 241–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57814-5_12.

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Conference papers on the topic "Clinical trials"

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Hilsenbeck, S., and M. Piccart. "Clinical Trials 102: New Strategies in Targeted Therapy Clinical Trials." In Abstracts: Thirty-Second Annual CTRC‐AACR San Antonio Breast Cancer Symposium‐‐ Dec 10‐13, 2009; San Antonio, TX. American Association for Cancer Research, 2009. http://dx.doi.org/10.1158/0008-5472.sabcs-09-es5-1.

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Cameron, Fraser, Nihat Baysal, and B. Wayne Bequette. "A differential simulator using past clinical trial data to run simulated clinical trials." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7172135.

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"Feasibility Estimation for Clinical Trials." In International Conference on Health Informatics. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004795500680077.

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Kiekens, Kelli C., Dominique Galvez, Gabriela Romano, Ricky Cordova, and Jennifer K. Barton. "Falloposcope Modifications for Clinical Trials." In CLEO: Applications and Technology. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/cleo_at.2020.am4i.5.

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Summers, Ron, Hiten Vyas, Nilesh Dudhal, Neil F. Doherty, Crispin R. Coombs, and Mark Hepworth. "Innovations in clinical trials informatics." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649806.

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Xing, Jasmine, and Ang Li. "Enhancing Clinical Trial Access and Diversity: An Innovative App for Personalized Trial Recommendations and Equitable Healthcare Participation." In 10th International Conference on Artificial Intelligence & Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.131906.

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This project focuses on creating an app to recommend clinical trials to users based on their profiles, improving access and diversity in trial participation. It addresses disparities in trial representation and aims to provide easy access to trial information. The app utilizes web scraping, a recommendation system, and a saving mechanism for trials. An experiment evaluates the recommendation system's accuracy, highlighting the challenges of ethnicity clustering. The app aims to promote equitable healthcare access and expand participation in clinical trials. Shortcomings of existing solutions include limited scope, such as being specific to cancer trials or certain patient groups. In contrast, the app aims to provide recommendations for a broader range of diseases and profiles, improving accessibility and diversity. Future improvements include refining the recommendation model, increasing the number of clusters, adding location-based recommendations, and expanding the trial database. The ultimate goal is to enhance the diversity of trial participants and improve healthcare equity.
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Bhati, D. K. "Informatics in clinical trials: Evidences from Clinical Data Management." In 2013 Indian Conference on Medical Informatics and Telemedicine (ICMIT). IEEE, 2013. http://dx.doi.org/10.1109/indiancmit.2013.6529402.

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"Modeling and Simulation in Clinical Trials." In 2016 Spring Simulation Multi-Conference. Society for Modeling and Simulation International (SCS), 2016. http://dx.doi.org/10.22360/springsim.2016.msm.002.

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Varese, A., M. Scaldaferri, E. Buffa, E. Ferrarato, MR Chiappetta, and F. Cattel. "CP-105 Clinical trials in haematology." In 22nd EAHP Congress 22–24 March 2017 Cannes, France. British Medical Journal Publishing Group, 2017. http://dx.doi.org/10.1136/ejhpharm-2017-000640.104.

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Brockenbrough, J. Scott, Libing Feng, Ingunn M. Stromnes, Kathleen E. DelGiorno, Martin C. Whittle, Carlos Cuevas, Ashley M. Dotson, Joseph S. Ryan, Shelley M. Thorsen, and Sunil R. Hingorani. "Abstract A92: Murine clinical trials program." In Abstracts: AACR Special Conference on Pancreatic Cancer: Innovations in Research and Treatment; May 18-21, 2014; New Orleans, LA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.panca2014-a92.

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Reports on the topic "Clinical trials"

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Kaplan, Celia P. Minorities in Clinical Trials: Patients, Physicians, Clinical Trial Characteristics, and Their Environment. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada542448.

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Kaplan, Celia. Minorities and Clinical Trials: Patients, Physicians, Clinical Trial Characteristics, and Their Environment. Fort Belvoir, VA: Defense Technical Information Center, July 2011. http://dx.doi.org/10.21236/ada573587.

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Kaplan, Celia P. Minorities and Clinical Trials: Patients, Physicians, Clinical Trial Characteristics and their Environment. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada586285.

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Keenan, Teresa A., and Chuck Rainville. AARP Clinical Trials Survey: Annotated Questionnaire. Washington, DC: AARP Research, July 2023. http://dx.doi.org/10.26419/res.00709.002.

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Kaplan, Celia P. Inclusion of Minority Patients in Breast Cancer Clinical Trials: The Role of the Clinical Trial Environment. Fort Belvoir, VA: Defense Technical Information Center, May 2008. http://dx.doi.org/10.21236/ada485604.

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Kaplan, Celia P. Inclusion of Minority Patients in Breast Cancer Clinical Trials: The Role of the Clinical Trial Environment. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada509888.

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Kaplan, Celia P. Inclusion of Minority Patients in Breast Cancer Clinical Trials: The Role of the Clinical Trial Environment. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada544195.

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Lefebvre, Carol, and Julie Glanville. Identifying Unpublished Clinical Trial Data for Evidence Syntheses. Instats Inc., 2023. http://dx.doi.org/10.61700/txz8zyhloguql469.

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Abstract:
This workshop provides an overview of identifying clinical trials and trial results for systematic reviews and other evidence syntheses. It covers sources such as trials / trials results registers, regulatory agency sources, and other novel resources. Participants will learn how to search the above sources, how to manage search results and report their searches. An official Instats certificate of completion and 1 ECTS Equivalent point is provided at the conclusion of the seminar.
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Harker, Y. D. Real-time monitoring for human clinical trials. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/421337.

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Zilliox, Patricia. Inherited Retinal Degenerative Disease Clinical Trials Network. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada618254.

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