Structures’ validation profiles in Transmission of Imaging and Data (TRIAD) for automated National Clinical Trials Network (NCTN) clinical trial digital data quality assurance

Part two of this series focuses on the remaining three exemplary attributes: quality assurance, multidisciplinary involvement in the clinical trial process, and clinical trials awareness programs.

Download Full-text

Guidance on the use of MRI for treatment planning in radiotherapy clinical trials

British Journal of Radiology ◽

10.1259/bjr.20190161 ◽

2020 ◽

Vol 93 (1105) ◽

pp. 20190161

Author(s):

Robert I Johnstone ◽

Teresa Guerrero-Urbano ◽

Andriana Michaelidou ◽

Tony Greener ◽

Elizabeth Miles ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Quality Assurance ◽

Treatment Planning ◽

Sufficient Information ◽

Planning Systems ◽

Imaging Guidance ◽

Safety Issues ◽

Treatment Planning Systems ◽

Trial Protocols

The aim of this article is to propose meaningful guidance covering the technical and safety issues involved when designing or conducting radiotherapy clinical trials that use MRI for treatment planning. The complexity of imaging requirements will depend on the trial aims, design and MRI methods used. The use of MRI within the RT pathway is becoming more prevalent and clinically appropriate as access to MRI increases, treatment planning systems become more versatile and potential indications for MRI-planning in RT are documented. Novel MRI-planning opportunities are often initiated and validated within clinical trials. The guidance in this document is intended to assist researchers designing RT clinical trials involving MRI, so that they may provide sufficient information about the appropriate methods to be used for image acquisition, post-processing and quality assurance such that participating sites complete MRI to consistent standards. It has been produced in collaboration with the National Radiotherapy Trials Quality Assurance Group (RTTQA). As the use of MRI in RT is developed, it is highly recommended for researchers writing clinical trial protocols to include imaging guidance as part of their clinical trial documentation covering the trial-specific requirements for MRI procedures. Many of the considerations and recommendations in this guidance may well apply to MR-guided treatment machines, where clinical trials will be crucial. Similarly, many of these recommendations will apply to the general use of MRI in RT, outside of clinical trials. This document contains a large number of recommendations, not all of which will be relevant to any particular trial. Designers of RT clinical trials must therefore take this into account. They must also use their own judgement as to the appropriate compromise between accessibility of the trial and its technical rigour.

Download Full-text

Applying Data Warehousing to a Phase III Clinical Trial From the Fondazione Italiana Linfomi Ensures Superior Data Quality and Improved Assessment of Clinical Outcomes

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00049 ◽

2019 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Gian Maria Zaccaria ◽

Simone Ferrero ◽

Samanta Rosati ◽

Marco Ghislieri ◽

Elisa Genuardi ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Data Quality ◽

Clinical Outcomes ◽

Control Strategy ◽

International Prognostic Index ◽

Prognostic Index ◽

Database Management System ◽

Phase Iii ◽

End Points

PURPOSE Data collection in clinical trials is becoming complex, with a huge number of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we used data warehouse (DW) concepts to achieve this goal. A DW was developed to accommodate data from a large clinical trial, including all the characteristics collected. We present the results related to baseline variables with the following objectives: developing a data quality (DQ) control strategy and improving outcome analysis according to the clinical trial primary end points. METHODS Data were retrieved from the electronic case reporting forms (eCRFs) of the phase III, multicenter MCL0208 trial (ClinicalTrials.gov identifier: NCT02354313 ) of the Fondazione Italiana Linfomi for younger patients with untreated mantle cell lymphoma (MCL). The DW was created with a relational database management system. Recommended DQ dimensions were observed to monitor the activity of each site to handle DQ management during patient follow-up. The DQ management was applied to clinically relevant parameters that predicted progression-free survival to assess its impact. RESULTS The DW encompassed 16 tables, which included 226 variables for 300 patients and 199,500 items of data. The tool allowed cross-comparison analysis and detected some incongruities in eCRFs, prompting queries to clinical centers. This had an impact on clinical end points, as the DQ control strategy was able to improve the prognostic stratification according to single parameters, such as tumor infiltration by flow cytometry, and even using established prognosticators, such as the MCL International Prognostic Index. CONCLUSION The DW is a powerful tool to organize results from large phase III clinical trials and to effectively improve DQ through the application of effective engineered tools.

Download Full-text

Scarce quality assurance documentation in major clinical trial registries for approved medicines used in post-marketing clinical trials

Trials ◽

10.1186/s13063-019-3277-8 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Yorokpa Joachim Doua ◽

Hanneke Dominicus ◽

Julius Mugwagwa ◽

Suzelle Magalie Gombe ◽

Jude Nwokike

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Quality Assurance ◽

Clinical Trial Registries ◽

Post Marketing ◽

Major Clinical Trial

Download Full-text

Exploring Data Quality Management within Clinical Trials

Applied Clinical Informatics ◽

10.1055/s-0037-1621702 ◽

2018 ◽

Vol 09 (01) ◽

pp. 072-081 ◽

Cited By ~ 7

Author(s):

Lauren Houston ◽

Yasmine Probst ◽

Ping Yu ◽

Allison Martin

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Quality Management ◽

Data Quality ◽

Staff Training ◽

Training Data ◽

Quality Monitoring ◽

Data Monitoring ◽

Quality Data ◽

Data Quality Management

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 trial sites, with the aim to develop standard data quality monitoring procedures to ensure data integrity. Methods Relevant information about data quality management methods and procedures, error levels, data monitoring, staff training, and development were collected. Staff members from 142 clinical trials listed on the National Health and Medical Research Council (NHMRC) clinical trials Web site were invited to complete a short self-reported semiquantitative anonymous online survey. Results Twenty (14%) clinical trials completed the survey. Results from the survey indicate that procedures to ensure data quality varies among clinical trial sites. Centralized monitoring (65%) was the most common procedure to ensure high-quality data. Ten (50%) trials reported having a data management plan in place and two sites utilized an error acceptance level to minimize discrepancy, set at <5% and 5 to 10%, respectively. The quantity of data variables checked (10–100%), the frequency of visits (once-a-month to annually), and types of variables (100%, critical data or critical and noncritical data audits) for data monitoring varied among respondents. The average time spent on staff training per person was 11.58 hours over a 12-month period and the type of training was diverse. Conclusion Clinical trial sites are implementing ad hoc methods pragmatically to ensure data quality. Findings highlight the necessity for further research into “standard practice” focusing on developing and implementing publicly available data quality monitoring procedures.

Download Full-text

Barriers, adoption, technology, impact and benefits of risk based monitoring

International Journal of Clinical Trials ◽

10.18203/2349-3259.ijct20160473 ◽

2016 ◽

Vol 3 (1) ◽

pp. 9 ◽

Cited By ~ 4

Author(s):

Bharat Kumar Shukla ◽

Mohammed Saleem Khan ◽

Veerabhadra Nayak

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Clinical Research ◽

Data Quality ◽

Drug Administration ◽

Food And Drug Administration ◽

Source Information ◽

The Us ◽

Previous Decade

The expense and unpredictability of clinical trials have increased drastically as of late. Up to third of a clinical trials expense can now be credited to the customary on location audit of trial information. While powerful observing is basic to ensuring the prosperity of trial members and keeping up the respectability of definite results, it is presently by and large acknowledged that the procedure for clinical trial checking needs to change. A more brought together, hazard based methodology is currently the favoured technique for monitoring clinical trials, as per a few administrative offices, including the US Food and Drug Administration (FDA). The movement has demonstrated overwhelming to numerous associations, nonetheless, and it is now and again not clear where to start. Over the previous decade, the clinical research industry's standard to meet regulators monitoring commitments has included continuous and normal onsite monitoring visits with 100% source information confirmation (SDV). The conviction that "more is better" proceeds with new proof that onsite monitoring practices don't inexorably ensure persistent wellbeing and data quality.

Download Full-text

Credentialing Proton Centers for Clinical Trials

10.5772/intechopen.95958 ◽

2021 ◽

Author(s):

Paige A. Taylor

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Quality Assurance ◽

Patient Outcomes ◽

Proton Therapy ◽

Quality Data ◽

Trial Participation ◽

Clinical Trial Participation ◽

High Quality ◽

High Quality Data

This chapter will provide an overview of quality assurance processes to credential proton therapy centers for clinical trial participation. There are a number of credentialing audit steps, including independent output verification, anthropomorphic phantom audits, image guidance credentialing, knowledge assessments, and on-site dosimetry review. The purpose of these credentialing steps is to ensure consistency across proton centers participating in clinical trials, and well as comparability with photon centers for randomized trials. This uniformity ensures high quality data for measuring patient outcomes, which are pivotal at a time when proton therapy is being assessed for superior outcomes.

Download Full-text