scholarly journals CLINICAL DATA MANAGEMENT IMPORTANCE IN CLINICAL RESEARCH

2016 ◽  
pp. 59
Author(s):  
Deepa Murugesan ◽  
Ranganath Banerjee ◽  
Gopal Ramesh Kumar
Keyword(s):  
Clinical Trial ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Drug Approval ◽  
Trial Data ◽  
Data Capture ◽  
Data Management System ◽  
Pharmaceutical Companies ◽  
Management Tools

<p>ABSTRACT<br />Over the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for their<br />new drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for their<br />medical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examine<br />and evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the most<br />valuable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reduces<br />the duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitates<br />data management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up the<br />clinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importance<br />of data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concluded<br />that data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials and<br />facilitates the drug discovery and development.<br />Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture.</p>

Clinical Trial Data Management Software: A Review of the Technical Features

2019 ◽  
Vol 14 (3) ◽  
pp. 160-172 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technical Features

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 related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

The development of CLINDATA, a clinical trial data management system

1981 ◽  
Vol 3 (3) ◽  
pp. 129-136 ◽  
Author(s):  
T. Ravenscroft ◽  
D.E. Smith
Keyword(s):  
Clinical Trial ◽  
Data Management ◽  
Management System ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Long Term Follow Up ◽  
On Line

The paper describes the design and implementation of a clinical trial data management system at the Wellcome Research Laboratories. Based on an IBM 3031 computer, the system provides the capability for on-line data input, search ing and comprehensive data analysis. The database also performs an adverse reaction reporting function and provides for long term follow-up of patients.

scholarly journals Data Validation and Verification Using Blockchain in a Clinical Trial for Breast Cancer: Regulatory Sandbox (Preprint)

2020 ◽  
Author(s):  
Tomonobu Hirano ◽  
Tomomitsu Motohashi ◽  
Kosuke Okumura ◽  
Kentaro Takajo ◽  
Taiyo Kuroki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Research Process ◽  
Current Data ◽  
Data Management System ◽  
Blockchain Technology

BACKGROUND The integrity of data in a clinical trial is essential, but the current data management process is too complex and highly labor-intensive. As a result, clinical trials are prone to consuming a lot of budget and time, and there is a risk for human-induced error and data falsification. Blockchain technology has the potential to address some of these challenges. OBJECTIVE The aim of the study was to validate a system that enables the security of medical data in a clinical trial using blockchain technology. METHODS We have developed a blockchain-based data management system for clinical trials and tested the system through a clinical trial for breast cancer. The project was conducted to demonstrate clinical data management using blockchain technology under the regulatory sandbox enabled by the Japanese Cabinet Office. RESULTS We verified and validated the data in the clinical trial using the validation protocol and tested its resilience to data tampering. The robustness of the system was also proven by survival with zero downtime for clinical data registration during a Amazon Web Services disruption event in the Tokyo region on August 23, 2019. CONCLUSIONS We show that our system can improve clinical trial data management, enhance trust in the clinical research process, and ease regulator burden. The system will contribute to the sustainability of health care services through the optimization of cost for clinical trials.

scholarly journals Data Validation and Verification Using Blockchain in a Clinical Trial for Breast Cancer: Regulatory Sandbox

10.2196/18938 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e18938
Author(s):  
Tomonobu Hirano ◽  
Tomomitsu Motohashi ◽  
Kosuke Okumura ◽  
Kentaro Takajo ◽  
Taiyo Kuroki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Research Process ◽  
Current Data ◽  
Data Management System ◽  
Blockchain Technology

Background The integrity of data in a clinical trial is essential, but the current data management process is too complex and highly labor-intensive. As a result, clinical trials are prone to consuming a lot of budget and time, and there is a risk for human-induced error and data falsification. Blockchain technology has the potential to address some of these challenges. Objective The aim of the study was to validate a system that enables the security of medical data in a clinical trial using blockchain technology. Methods We have developed a blockchain-based data management system for clinical trials and tested the system through a clinical trial for breast cancer. The project was conducted to demonstrate clinical data management using blockchain technology under the regulatory sandbox enabled by the Japanese Cabinet Office. Results We verified and validated the data in the clinical trial using the validation protocol and tested its resilience to data tampering. The robustness of the system was also proven by survival with zero downtime for clinical data registration during a Amazon Web Services disruption event in the Tokyo region on August 23, 2019. Conclusions We show that our system can improve clinical trial data management, enhance trust in the clinical research process, and ease regulator burden. The system will contribute to the sustainability of health care services through the optimization of cost for clinical trials.

scholarly journals Heterogeneity prevails: the state of clinical trial data management in Europe - results of a survey of ECRIN centres

Trials ◽  
2010 ◽  
Vol 11 (1) ◽  
Author(s):  
Wolfgang Kuchinke ◽  
Christian Ohmann ◽  
Qin Yang ◽  
Nader Salas ◽  
Jens Lauritsen ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Data Management ◽  
Clinical Trial Data ◽  
Trial Data ◽  
The State

scholarly journals Sharing of clinical trial data and results reporting practices among large pharmaceutical companies: cross sectional descriptive study and pilot of a tool to improve company practices

BMJ ◽  
2019 ◽  
pp. l4217 ◽  
Author(s):  
Jennifer Miller ◽  
Joseph S Ross ◽  
Marc Wilenzick ◽  
Michelle M Mello
Keyword(s):  
Clinical Trial ◽  
Data Sharing ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Interquartile Range ◽  
Pharmaceutical Companies ◽  
Cross Sectional ◽  
New Drug ◽  
Results Reporting ◽  
Policies And Practices

Abstract Objectives To develop and pilot a tool to measure and improve pharmaceutical companies’ clinical trial data sharing policies and practices. Design Cross sectional descriptive analysis. Setting Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015. Data sources Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies. Main outcome measures Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices. Results Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval. Conclusions It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study’s measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.

82 Internet security in clinical trial data management systems

1997 ◽  
Vol 18 (3) ◽  
pp. S92 ◽  
Author(s):  
Jeffrey P. Martin ◽  
Patrick Beighley ◽  
David C. Hiriak ◽  
Eugene D. Spadafore ◽  
Kimberly C. Beringer
Keyword(s):  
Clinical Trial ◽  
Data Management ◽  
Clinical Trial Data ◽  
Internet Security ◽  
Trial Data ◽  
Management Systems ◽  
Data Management Systems

Carfilzomib

Blood ◽  
2013 ◽  
Vol 121 (6) ◽  
pp. 893-897 ◽  
Author(s):  
K. Martin Kortuem ◽  
A. Keith Stewart
Keyword(s):  
Clinical Trial ◽  
Multiple Myeloma ◽  
Disease Progression ◽  
Drug Administration ◽  
Proteasome Inhibitor ◽  
Food And Drug Administration ◽  
Clinical Trial Data ◽  
Drug Approval ◽  
Trial Data ◽  
The Us

Abstract This spotlight review focuses on the second-generation proteasome inhibitor carfilzomib, which was recently approved by the US Food and Drug Administration for treatment of relapsed and refractory multiple myeloma patients who have received at least 2 prior therapies, including bortezomib and an immunomodulatory agent, and have demonstrated disease progression on or within 60 days of the completion of the last therapy. This review focuses on clinical trial data leading to drug approval and provides advice for treating physicians who are now accessing this drug for patients.

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