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Journal articles on the topic 'Clinical trials data'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Catanzaro, Linda M., and Gene D. Morse. "Data from Clinical Trials." JAIDS Journal of Acquired Immune Deficiency Syndromes 38, Supplement 1 (2005): S26—S29. http://dx.doi.org/10.1097/01.qai.0000167036.48772.c8.

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2

Houston, Lauren, Yasmine Probst, Ping Yu, and Allison Martin. "Exploring Data Quality Management within Clinical Trials." Applied Clinical Informatics 09, no. 01 (2018): 072–81. http://dx.doi.org/10.1055/s-0037-1621702.

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Background Clinical trials are an important research method for improving medical knowledge and patient care. Multiple international and national guidelines stipulate the need for data quality and assurance. Many strategies and interventions are developed to reduce error in trials, including standard operating procedures, personnel training, data monitoring, and design of case report forms. However, guidelines are nonspecific in the nature and extent of necessary methods. Objective This article gathers information about current data quality tools and procedures used within Australian clinical

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3

Burgess, David C., Val J. Gebski, and Anthony C. Keech. "Baseline data in clinical trials." Medical Journal of Australia 179, no. 2 (2003): 105–7. http://dx.doi.org/10.5694/j.1326-5377.2003.tb05447.x.

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4

George, Stephen L., and Marc Buyse. "Data fraud in clinical trials." Clinical Investigation 5, no. 2 (2015): 161–73. http://dx.doi.org/10.4155/cli.14.116.

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5

Wilcox, Allen, and Sholom Wacholder. "Observational Data and Clinical Trials." Epidemiology 19, no. 6 (2008): 765. http://dx.doi.org/10.1097/ede.0b013e318188e872.

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6

Albrich, J. Michael. "Data Bases of Clinical Trials." New England Journal of Medicine 340, no. 1 (1999): 65–66. http://dx.doi.org/10.1056/nejm199901073400119.

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7

Song, Juwon. "Clinical trials with missing data." Biometrics 71, no. 2 (2015): 558–59. http://dx.doi.org/10.1111/biom.12319.

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8

RESNIK, DAVID. "Data Falsification in Clinical Trials." Science Communication 18, no. 1 (1996): 49–58. http://dx.doi.org/10.1177/1075547096018001003.

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9

Wilson, J. R. "Data audit for clinical trials." JAMA: The Journal of the American Medical Association 270, no. 22 (1993): 2685b—2685. http://dx.doi.org/10.1001/jama.270.22.2685b.

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10

Wilson, John R. "Data Audit for Clinical Trials." JAMA: The Journal of the American Medical Association 270, no. 22 (1993): 2685. http://dx.doi.org/10.1001/jama.1993.03510220041025.

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11

Nelausen, Knud Mejer, Hanne Marie Michelsen, Birgitte Krogh Jensen, Lisa Sengeløv, Marie-Helene Olsen, and Dorte Lisbet Nielsen. "Data quality in clinical trials." Journal of Clinical Oncology 32, no. 15_suppl (2014): e17610-e17610. http://dx.doi.org/10.1200/jco.2014.32.15_suppl.e17610.

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12

Mannucci, P. M. "Sharing data of clinical trials." European Journal of Internal Medicine 29 (April 2016): 1–2. http://dx.doi.org/10.1016/j.ejim.2016.02.015.

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13

Prisco, Domenico, Gabriele Ciuti, Elisa Grifoni, Elena Silvestri, and Giacomo Emmi. "Sharing data of clinical trials." European Journal of Internal Medicine 33 (September 2016): e25-e26. http://dx.doi.org/10.1016/j.ejim.2016.05.012.

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14

Seow, Hsien-Yeang, Patrick Whelan, Mark N. Levine, et al. "Funding Oncology Clinical Trials: Are Cooperative Group Trials Sustainable?" Journal of Clinical Oncology 30, no. 13 (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 calcula

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15

Bassion, Susan. "Standardizing Laboratory Data Interchange in Clinical Trials." JALA: Journal of the Association for Laboratory Automation 7, no. 5 (2002): 62–64. http://dx.doi.org/10.1016/s1535-5535-04-00219-9.

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The Clinical Data Interchange Standards Consortium has developed a Laboratory Model for laboratory data that is generated during the conduct of clinical trials. The Laboratory Model is the first step in proposing standards for the interchange of clinical trial laboratory data. Standards will decrease the time and resources required by stakeholders in the pharmaceutical development process (pharmaceutical companies, biotechnology companies, contract research organizations and laboratories). Standardization will therefore contain costs as well as improve data quality.

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16

Stensland, Kristian, Samuel Kaffenberger, David Canes, Matthew Galsky, Ted Skolarus, and Alireza Moinzadeh. "Assessing Genitourinary Cancer Clinical Trial Accrual Sufficiency Using Archived Trial Data." JCO Clinical Cancer Informatics, no. 4 (September 2020): 614–22. http://dx.doi.org/10.1200/cci.20.00031.

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PURPOSE Clinical trials often fail to reach their anticipated end points, most frequently because of poor accrual. Prior studies have analyzed trial termination, but it has not been easy to assess accrual estimates using international databases such as ClinicalTrials.gov because of limitations in accessing accrual information. Specifically, it is not easy to extract both anticipated and actual accrual of clinical trials. We designed a new algorithmic approach to extracting trial accrual data from ClinicalTrials.gov and used it to estimate the sufficiency of patient accrual onto genitourinary (

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17

Dalrymple, H. W. "The general data protection regulation, the clinical trial regulation and some complex interplay in paediatric clinical trials." European Journal of Pediatrics 180, no. 5 (2021): 1371–79. http://dx.doi.org/10.1007/s00431-021-03933-3.

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AbstractAlthough a number of authors have commented upon the impact of the GDPR on clinical trial conduct, few have examined the specific setting of paediatric trials. Whilst the general principles are the same as those for adults, some additional considerations arise. The ages of consent relating to data privacy and clinical trial participation are different in a number of countries, but the distinction is often not recognised in non-drug trials. Accidental pregnancies in clinical trials always raise complexities, but these are amplified when the trial subject is a minor, and the processes de

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18

Tsai, Kao-Tai, and Karl Peace. "Analysis of Subgroup Data of Clinical Trials." Journal of Causal Inference 1, no. 2 (2013): 193–207. http://dx.doi.org/10.1515/jci-2012-0008.

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AbstractLarge randomized controlled clinical trials are the gold standard to evaluate and compare the effects of treatments. It is common practice for investigators to explore and even attempt to compare treatments, beyond the first round of primary analyses, for various subsets of the study populations based on scientific or clinical interests to take advantage of the potentially rich information contained in the clinical database. Although subjects are randomized to treatment groups in clinical trials, this does not imply the same degree of randomization among sub-populations of the original

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19

Nourani, Aynaz, Haleh Ayatollahi, and Masoud Solaymani Dodaran. "Clinical Trial Data Management Software: A Review of the Technical Features." Reviews on Recent Clinical Trials 14, no. 3 (2019): 160–72. http://dx.doi.org/10.2174/1574887114666190207151500.

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Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers re

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20

Fleming, Thomas R. "Addressing Missing Data in Clinical Trials." Annals of Internal Medicine 154, no. 2 (2011): 113. http://dx.doi.org/10.7326/0003-4819-154-2-201101180-00010.

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21

van der Laan, Mark, and Todd J. Lorenz. "Addressing Missing Data in Clinical Trials." Annals of Internal Medicine 155, no. 2 (2011): 135. http://dx.doi.org/10.7326/0003-4819-155-2-201107190-00016.

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22

Katz, Terry L. "Missing Data in Clinical Trials Forum." Clinical Investigation 5, no. 8 (2015): 681–85. http://dx.doi.org/10.4155/cli.15.35.

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23

Bath, PhilipM W., Peter Sandercock, and Carl Counsell. "Storage of data from clinical trials." Lancet 346, no. 8976 (1995): 705. http://dx.doi.org/10.1016/s0140-6736(95)92316-0.

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24

Taves, Donald R. "Making data from clinical trials available." Clinical Trials 11, no. 6 (2014): 685. http://dx.doi.org/10.1177/1740774514547100.

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25

Krumholz, Harlan M., and Eric D. Peterson. "Open Access to Clinical Trials Data." JAMA 312, no. 10 (2014): 1002. http://dx.doi.org/10.1001/jama.2014.9647.

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26

Taichman, Darren B., Peush Sahni, Anja Pinborg, et al. "Data Sharing Statements for Clinical Trials." JAMA 317, no. 24 (2017): 2491. http://dx.doi.org/10.1001/jama.2017.6514.

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27

Rosenkranz, Gerd. "Modeling laboratory data from clinical trials." PAMM 7, no. 1 (2007): 1080301–2. http://dx.doi.org/10.1002/pamm.200700792.

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28

Weiss, Raymond B. "Data Audit for Clinical Trials-Reply." JAMA: The Journal of the American Medical Association 270, no. 22 (1993): 2685. http://dx.doi.org/10.1001/jama.1993.03510220041026.

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29

Metha, A. C., and J. Marsh. "Data Collection Forms in Clinical Trials." Cleveland Clinic Journal of Medicine 59, no. 4 (1992): 428. http://dx.doi.org/10.3949/ccjm.59.4.428.

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30

Tucker, Jane, and Iain Menzies Smith. "Electronic Data Transfer for Clinical Trials." Drug Information Journal 28, no. 2 (1994): 391–95. http://dx.doi.org/10.1177/009286159402800207.

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31

Best, Mark A. "Data Monitoring Committees in Clinical Trials." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 3 (2003): 406–7. http://dx.doi.org/10.1111/1467-9884.00369_7.

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32

Elbourne, D. "Data Collection Forms in Clinical Trials." Journal of Epidemiology & Community Health 46, no. 5 (1992): 547. http://dx.doi.org/10.1136/jech.46.5.547.

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33

Flyer, Paul, and Joseph Hirman. "Missing Data in Confirmatory Clinical Trials." Journal of Biopharmaceutical Statistics 19, no. 6 (2009): 969–79. http://dx.doi.org/10.1080/10543400903242746.

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34

de Pauw, M. "Data collection forms in clinical trials." Controlled Clinical Trials 13, no. 5 (1992): 351–52. http://dx.doi.org/10.1016/0197-2456(92)90037-z.

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35

Whitehead, J. "Monitoring and evaluating clinical trials data." Intensive Care Medicine 26 (February 24, 2000): S084—S088. http://dx.doi.org/10.1007/s001340051123.

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36

Sleight, Peter. "Data-monitoring committees in clinical trials." Lancet 341, no. 8854 (1993): 1219–20. http://dx.doi.org/10.1016/0140-6736(93)91053-o.

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37

Rosenkranz, Gerd K. "Modeling laboratory data from clinical trials." Computational Statistics & Data Analysis 53, no. 3 (2009): 812–19. http://dx.doi.org/10.1016/j.csda.2008.07.005.

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38

Huser, Vojtech, and Dikla Shmueli-Blumberg. "Data sharing platforms for de-identified data from human clinical trials." Clinical Trials 15, no. 4 (2018): 413–23. http://dx.doi.org/10.1177/1740774518769655.

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Data sharing of de-identified individual participant data is being adopted by an increasing number of sponsors of human clinical trials. In addition to standardizing data syntax for shared trial data, semantic integration of various data elements is the focus of several initiatives that define research common data elements. This perspective article, in the first part, compares several data sharing platforms for de-identified clinical research data in terms of their size, policies and supported features. In the second part, we use a case study approach to describe in greater detail one data sha

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39

MacKellar, Bonnie, Christina Schweikert, and Soon Ae Chun. "Patient-Centered Clinical Trials Decision Support using Linked Open Data." International Journal of Software Science and Computational Intelligence 6, no. 3 (2014): 31–48. http://dx.doi.org/10.4018/ijssci.2014070103.

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Patients often want to participate in relevant clinical trials for new or more effective alternative treatments. The clinical search system made available by the NIH is a step forward to support the patient's decision making, but, it is difficult to use and requires the patient to sift through lengthy text descriptions for relevant information. In addition, patients deciding whether to pursue a given trial often want more information, such as drug information. The authors' overall aim is to develop an intelligent patient-centered clinical trial decision support system. Their approach is to int

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40

Mockus, Susan M., Sara E. Patterson, Cara Statz, Carol J. Bult, and Gregory J. Tsongalis. "Clinical Trials in Precision Oncology." Clinical Chemistry 62, no. 3 (2016): 442–48. http://dx.doi.org/10.1373/clinchem.2015.247437.

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Abstract BACKGROUND Availability of genomic information used in the management of cancer treatment has outpaced both regulatory and reimbursement efforts. Many types of clinical trials are underway to validate the utility of emerging genome-based biomarkers for diagnostic, prognostic, and predictive applications. Clinical trials are a key source of evidence required for US Food and Drug Administration approval of therapies and companion diagnostics and for establishing the acceptance criteria for reimbursement. CONTENT Determining the eligibility of patients for molecular-based clinical trials

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41

Fujii, Hitoshi, Keiko Yukawa, and Hajime Sato. "International Comparison of Data from International Clinical Trials Registry Platform-Registered Clinical Trials." Health 08, no. 15 (2016): 1759–65. http://dx.doi.org/10.4236/health.2016.815169.

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42

Kasper, Siegfried. "Clinical data in early intervention." International Psychogeriatrics 24, S1 (2012): S41—S45. http://dx.doi.org/10.1017/s1041610212000555.

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ABSTRACTResearch into early intervention for Alzheimer's disease (AD) and dementia has involved cohort data from large epidemiological studies and data from specifically designed intervention trials. Cohort data indicate that use of nootropics and Ginkgo biloba extract may be associated with a reduced incidence of dementia and death. Data from large trials have often been inconclusive due to issues with poor medication adherence. However, such trials do indicate potential benefits with Gingko biloba extract in terms of reduced incidence of dementia of the AD type, vascular dementia and mixed p

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43

Moffitt, K., R. D. Marsh, and C. Hudson. "The case for proactive, collaborative data-updating in a statewide cancer clinical trial matching service." Journal of Clinical Oncology 25, no. 18_suppl (2007): 6570. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.6570.

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6570 Background Florida has the second highest cancer incidence of any state in the United States. To address Florida's growing cancer burden and the need for increased clinical trial participation, Florida Cancer Trials (FCT) launched a web-based and phone-based Clinical Trial Matching Service for the State of Florida on November 1, 2004 (operated by EmergingMed). After 2 years of monthly outreach to 96 trial sites, the FCT wanted to assess the outcome and ongoing need for intensive, grass-roots data verification activities in light of other publicly available cancer clinical trial databases.

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44

Shortreed, Susan M., Carolyn M. Rutter, Andrea J. Cook, and Gregory E. Simon. "Improving pragmatic clinical trial design using real-world data." Clinical Trials 16, no. 3 (2019): 273–82. http://dx.doi.org/10.1177/1740774519833679.

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Background Pragmatic clinical trials often use automated data sources such as electronic health records, claims, or registries to identify eligible individuals and collect outcome information. A specific advantage that this automated data collection often yields is having data on potential participants when design decisions are being made. We outline how this data can be used to inform trial design. Methods Our work is motivated by a pragmatic clinical trial evaluating the impact of suicide-prevention outreach interventions on fatal and non-fatal suicide attempts in the 18 months after randomi

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45

Radulovic, Sinisa. "Good clinical practice: International quality standard for clinical trials." Serbian Dental Journal 50, no. 1 (2003): 34–38. http://dx.doi.org/10.2298/sgs0301034r.

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A clinical trial is one of the most important examples of experimental studies. Clinical trials represent an indispensable tool for testing, in a rigorous scientific manner, the efficacy of new therapies. Good Clinical Practice is an international ethical and scientific quality standard for clinical trials, concerning the design, conduct, performance, monitoring auditing, recording, analysis and reporting. This is an assurance to the public that the rights, safety and well-being of trial subjects are protected, and that clinical trial data is credible. The above definitions are consistent with

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46

Sun, Yingcheng, Alex Butler, Ibrahim Diallo, et al. "A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data." Applied Clinical Informatics 12, no. 04 (2021): 816–25. http://dx.doi.org/10.1055/s-0041-1733846.

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Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming f

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47

Stein, Israel M., Crystal Sloan, Chester King, Edna Shattuck, and Daniel Doxtader. "Computerized Management of Clinical Trials and Patient Data: The Clinical Data Manager." Clinical Research Practices and Drug Regulatory Affairs 5, no. 4 (1987): 203–30. http://dx.doi.org/10.3109/10601338709019649.

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48

Kokosy, A., P. Saey, G. Tagne, et al. "Open and Configurable Heterogeneous Data Recording Device for Clinical Trials." Modelling, Measurement and Control C 81, no. 1-4 (2020): 62–66. http://dx.doi.org/10.18280/mmc_c.811-411.

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This article presents a heterogeneous, open and configurable data recording device to facilitate the implementation of clinical trials. It includes an embedded system connected via USB to an Android device, and a database in the Cloud. Using an Android application, it is possible to configure each clinical trial according to the needs of the test protocol and the people involved in the study. This device ensures the recording and storage of data from both the various sensors used and the questionnaires. This original device was used during the implementation of the clinical trials for a powere

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49

Radosa, Julia Caroline, Lisa Stotz, Carolin Müller, Askin Canguel Kaya, Erich-Franz Solomayer, and Marc Philipp Radosa. "Clinical Data on Immunotherapy in Breast Cancer." Breast Care 15, no. 5 (2020): 450–69. http://dx.doi.org/10.1159/000511788.

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<b><i>Background:</i></b> Breast cancer has traditionally been considered to have a low immunogenic potential compared to other tumor entities. <b><i>Summary:</i></b> The most extensively studied immunotherapeutic agents for breast cancer to date are immune checkpoint inhibitors, with the results of the IMpassion130 trial leading to the approval of atezolizumab plus nab-paclitaxel for first-line treatment of programmed cell death ligand 1-positive, metastatic, triple-negative breast cancer, and studies in earlier stages have yielded promising res

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50

Suvarna, VirajRamesh. "Sharing individual patient data from clinical trials." Perspectives in Clinical Research 6, no. 2 (2015): 71. http://dx.doi.org/10.4103/2229-3485.153996.

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