Revising the ECRIN standard requirements for information technology and data management in clinical trials.
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ABSTRACT:
SUBMITTER: Ohmann C
PROVIDER: S-EPMC3653810 | biostudies-other | 2013
REPOSITORIES: biostudies-other
Publications
Revising the ECRIN standard requirements for information technology and data management in clinical trials.
Ohmann Christian C Canham Steve S Cornu Catherine C Dreß Jochen J Gueyffier François F Kuchinke Wolfgang W Nicolis Enrico B EB Wittenberg Michael M
Trials 20130405
The pilot phase of the ECRIN (European Clinical Research Infrastructure Network) certification programme for European data centres, in late 2011, led to a substantial revision of the original ECRIN standards, completed by June 2012. The pilot phase, the conclusions drawn from it and the revised set of standards are described. Issues concerning the further development of standards and related material are discussed, as are the methods available to best support that development. A strategy is outl ...[more]
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Project description:BackgroundA recent survey has shown that data management in clinical trials performed by academic trial units still faces many difficulties (e.g. heterogeneity of software products, deficits in quality management, limited human and financial resources and the complexity of running a local computer centre). Unfortunately, no specific, practical and open standard for both GCP-compliant data management and the underlying IT-infrastructure is available to improve the situation. For that reason the "Working Group on Data Centres" of the European Clinical Research Infrastructures Network (ECRIN) has developed a standard specifying the requirements for high quality GCP-compliant data management in multinational clinical trials.MethodsInternational, European and national regulations and guidelines relevant to GCP, data security and IT infrastructures, as well as ECRIN documents produced previously, were evaluated to provide a starting point for the development of standard requirements. The requirements were produced by expert consensus of the ECRIN Working group on Data Centres, using a structured and standardised process. The requirements were divided into two main parts: an IT part covering standards for the underlying IT infrastructure and computer systems in general, and a Data Management (DM) part covering requirements for data management applications in clinical trials.ResultsThe standard developed includes 115 IT requirements, split into 15 separate sections, 107 DM requirements (in 12 sections) and 13 other requirements (2 sections). Sections IT01 to IT05 deal with the basic IT infrastructure while IT06 and IT07 cover validation and local software development. IT08 to IT015 concern the aspects of IT systems that directly support clinical trial management. Sections DM01 to DM03 cover the implementation of a specific clinical data management application, i.e. for a specific trial, whilst DM04 to DM12 address the data management of trials across the unit. Section IN01 is dedicated to international aspects and ST01 to the competence of a trials unit's staff.ConclusionsThe standard is intended to provide an open and widely used set of requirements for GCP-compliant data management, particularly in academic trial units. It is the intention that ECRIN will use these requirements as the basis for the certification of ECRIN data centres.
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Project description:The purposes are to manage human resource data better and explore the association between Human Resource Management (HRM), data mining, and economic management. An Ensemble Classifier-Decision Tree (EC-DT) algorithm is proposed based on the single decision tree algorithm to analyze HRM data. The involved single decision tree algorithms include C4.5, Random Tree, J48, and SimpleCart. Then, an HRM system is established based on the designed algorithm, and the evaluation management and talent recommendation modules are tested. Finally, the designed algorithm is compared and tested. Experimental results suggest that C4.5 provides the highest classification accuracy among the single decision tree algorithms, reaching 76.69%; in contrast, the designed EC-DT algorithm can provide a classification accuracy of 79.97%. The proposed EC-DT algorithm is compared with the Content-based Recommendation Method (CRM) and the Collaborative Filtering Recommendation Method (CFRM), revealing that its Data Mining Recommendation Method (DMRM) can provide the highest accuracy and recall, reaching 35.2% and 41.6%, respectively. Therefore, the data mining-based HRM system can promote and guide enterprises to develop according to quantitative evaluation results. The above results can provide a reference for studying HRM systems based on data mining technology.
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Project description:BackgroundDespite the proliferation of health information technology (IT) interventions, descriptions of the unique considerations for conducting randomized trials of health IT interventions intended for patient use are lacking.PurposeOur purpose is to describe the protocol to evaluate Pocket PATH (Personal Assistant for Tracking Health), a novel health IT intervention, as an exemplar of how to address issues that may be unique to a randomized controlled trial (RCT) to evaluate health IT intended for patient use.MethodsAn overview of the study protocol is presented. Unique considerations for health IT intervention trials and strategies are described to maintain equipoise, to monitor data safety and intervention fidelity, and to keep pace with changing technology during such trials.Lessons learnedThe sovereignty granted to technology, the rapid pace of changes in technology, ubiquitous use in health care, and obligation to maintain the safety of research participants challenge researchers to address these issues in ways that maintain the integrity of intervention trials designed to evaluate the impact of health IT interventions intended for patient use.ConclusionsOur experience evaluating the efficacy of Pocket PATH may provide practical guidance to investigators about how to comply with established procedures for conducting RCTs and include strategies to address the unique issues associated with the evaluation of health IT for patient use.
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Project description:IntroductionNumerous arguments have been advanced for broadly sharing de-identified, participant-level clinical trial data. However, data sharing in pragmatic clinical trials (PCTs) presents ethical challenges. While prior scholarship has described aspects of PCTs that raise distinct considerations for data sharing, there have been no reports of the experiences of those at the leading edge of data-sharing efforts for PCTs, including how these particular challenges have been navigated. To address this gap, we conducted interviews with key stakeholders, with a focus on the ethical issues presented by sharing data from PCTs.MethodsWe recruited respondents using purposive sampling to reflect the range of stakeholder groups affected by efforts to expand PCT data sharing. Through semi-structured interviews, we explored respondents' experiences and perceptions about sharing de-identified, individual-level data from PCTs. An integrated approach was used to identify and describe key themes.ResultsWe conducted 40 interviews between April and September 2022. Five overarching themes emerged through analysis: (1) challenges in sharing data collected under a waiver or alteration of consent; (2) conflicting views regarding PCT patient-subject preferences for data sharing; (3) identification of respect-promoting practices beyond consent; (4) concerns about elevated risks or burdens from sharing PCT data; and (5) diverse views about the likely benefits resulting from sharing PCT data.ConclusionOur data indicate unresolved tensions in how to fulfill the expectation to broadly share de-identified, individual-level data from PCTs, and suggest that those promulgating and implementing data-sharing policies must be sensitive to PCT-specific considerations. Future work could inform efforts to tailor data-sharing policy and practice to reflect the challenges presented by PCTs, including sharing experiences from trials that have successfully navigated these tensions.
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Project description:BACKGROUND:Blockchain technology is emerging as an innovative tool in data and software security. OBJECTIVE:This study aims to explore the role of blockchain in supporting clinical trials data management and develop a proof-of-concept implementation of a patient-facing and researcher-facing system. METHODS:Blockchain-based Smart Contracts were built using the Ethereum platform. RESULTS:We described BlockTrial, a system that uses a Web-based interface to allow users to run trials-related Smart Contracts on an Ethereum network. Functions allow patients to grant researchers access to their data and allow researchers to submit queries for data that are stored off chain. As a type of distributed ledger, the system generates a durable and transparent log of these and other transactions. BlockTrial could be used to increase the trustworthiness of data collected during clinical research with benefits to researchers, regulators, and drug companies alike. In addition, the system could empower patients to become more active and fully informed partners in research. CONCLUSIONS:Blockchain technology presents an opportunity to address some of the common threats to the integrity of data collected in clinical trials and ensure that the analysis of these data comply with prespecified plans. Further technical work is needed to add additional functions. Policies must be developed to determine the optimal models for participation in the system by its various stakeholders.
| S-EPMC6320404 | biostudies-literature
Project description:Audits are often performed to assess the quality of clinical trial data, but beyond detecting fraud or sloppiness, the audit data are generally ignored. In an earlier study, using data from a nonrandomized study, Shepherd and Yu developed statistical methods to incorporate audit results into study estimates and demonstrated that audit data could be used to eliminate bias.In this article, we examine the usefulness of audit-based error-correction methods in clinical trial settings where a continuous outcome is of primary interest.We demonstrate the bias of multiple linear regression estimates in general settings with an outcome that may have errors and a set of covariates for which some may have errors and others, including treatment assignment, are recorded correctly for all subjects. We study this bias under different assumptions, including independence between treatment assignment, covariates, and data errors (conceivable in a double-blinded randomized trial) and independence between treatment assignment and covariates but not data errors (possible in an unblinded randomized trial). We review moment-based estimators to incorporate the audit data and propose new multiple imputation estimators. The performance of estimators is studied in simulations.When treatment is randomized and unrelated to data errors, estimates of the treatment effect using the original error-prone data (i.e., ignoring the audit results) are unbiased. In this setting, both moment and multiple imputation estimators incorporating audit data are more variable than standard analyses using the original data. In contrast, in settings where treatment is randomized but correlated with data errors and in settings where treatment is not randomized, standard treatment-effect estimates will be biased. And in all settings, parameter estimates for the original, error-prone covariates will be biased. The treatment and covariate effect estimates can be corrected by incorporating audit data using either the multiple imputation or moment-based approaches. Bias, precision, and coverage of confidence intervals improve as the audit size increases.The extent of bias and the performance of methods depend on the extent and nature of the error as well as the size of the audit. This study only considers methods for the linear model. Settings much different than those considered here need further study.In randomized trials with continuous outcomes and treatment assignment independent of data errors, standard analyses of treatment effects will be unbiased and are recommended. However, if treatment assignment is correlated with data errors or other covariates, naive analyses may be biased. In these settings, and when covariate effects are of interest, approaches for incorporating audit results should be considered.
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Project description:BACKGROUND:For patients with diabetes, the self-monitoring of blood glucose (SMBG) is a recommended way of controlling the blood glucose level. By leveraging the modern information and communication technology (ICT) and the corresponding infrastructure, engineers nowadays are able to merge the SMBG activities into daily life and to dramatically reduce patient's burden. Such type of ICT-powered SMBG had already been marketed in the United States and the European Union for a decade, but was introduced into the Chinese market only in recent years. Although there is no doubt about the general need for such type of SMBG in the Chinese market, how it could be adapted to the local technical and operational environment is still an open question. OBJECTIVE:Our overall goal is to understand the local requirements and the current status of deploying ICT-powered SMBG to the Chinese market. In particular, we aim to analyze existing domestic SMBG mobile apps and relevant domestic patents to identify their various aspects, including the common functionalities, innovative feature, defects, conformance to standards, prospects, etc. In the long run, we hope the outcome of this study could help the decision making on how to properly adapt ICT-powered SMBG to the Chinese market. METHODS:We identified 289 apps. After exclusion of irrelevant apps, 78 apps remained. These were downloaded and analyzed. A total of 8070 patents related to glucose were identified from patent database. Irrelevant materials and duplicates were excluded, following which 39 patents were parsed to extract the important features. These apps and patents were further compared with the corresponding requirements derived from relevant clinical guidelines and data standards. RESULTS:The most common features of studied apps were blood health data recording, notification, and decision supporting. The most common features of studied patents included mobile terminal, server, and decision supporting. The main difference between patents and apps is that the patents had 2 specific features, namely, interface to the hospital information system and recording personal information, which were not mentioned in the app. The other major finding is that, in general, in terms of the components of the features, although the features identified in both apps and patents conform to the requirements of the relevant clinical guidelines and data standards, upon looking into the details, gaps exist between the features of the identified apps and patents and the relevant clinical guidelines and data standards. In addition, the social media feature that the apps and patents have is not included in the standard requirements list. CONCLUSIONS:The development of Chinese SMBG mobile apps and relevant patents is still in the primitive stage. Although the functionalities of most apps and patents can meet the basic requirements of SMBG, gaps have been identified when comparing the functionalities provided by apps and patents with the requirements necessitated by the standards. One of the most important gaps is that only a small portion of the studied apps provides the automatic data transmission and exchange feature, which may hamper the overall performance. The clinical guidelines can thus be further developed to leverage new features provided by ICT-powered SMBG apps (eg, the social media feature, which may help to improve the social intervention of patients with diabetes).
| S-EPMC6238845 | biostudies-other
Project description:Background: There has been an increasing requirement for fresh tumor tissue to enroll in clinical trials in order to look for specific biomarkers. This has been shown to increase screening duration and increase screen failure rates. It was important to corroborate these results in other centers. Methods: This study is a non-randomized retrospective analysis of patients in one subset of patients seen by research nurses who operated in the standard head/neck and lung team not including patients in the phase 1 program. All patients were enrolled in clinical trials from January 16, 2013 to May 28, 2018 at USC Norris Comprehensive Cancer Institute in Los Angeles. Patients who were required to give fresh research biopsies prior to intervention were part of the research biopsy group. Results: In total, 76 patients were analyzed in this study. Thirty-three patients were in the research biopsygroup and 43 patients were in the no biopsy group. Trials that required a fresh biopsy had a longer median screening duration (30 vs. 14 days) than trials that did not require a biopsy (p < 0.0001). Conclusions: Our study shows that requiring biopsies prior to clinical trial treatment results in a statistically significant delay in treatment. The informed consent forms that were part of clinical trials involving mandatory research biopsies did not reflect this delay in treatment. However, these delays did not result in a statistically significant decrease in number of days on trial or days until progression of disease.
| S-EPMC6813196 | biostudies-literature
Project description:Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials.
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Project description:BACKGROUND:Clinical trials are one of the most challenging and meaningful designs in medical research. One essential step before starting a clinical trial is screening, that is, to identify patients who fulfill the inclusion criteria and do not fulfill the exclusion criteria. The screening step for clinical trials might be supported by modern information technology (IT). OBJECTIVE:This explorative study aimed (1) to obtain insights into which tools for feasibility estimations and patient screening are actually used in clinical routine and (2) to determine which method and type of IT support could benefit clinical staff. METHODS:Semistandardized interviews were conducted in 5 wards (cardiology, gynecology, gastroenterology, nephrology, and palliative care) in a German university hospital. Of the 5 interviewees, 4 were directly involved in patient screening. Three of them were clinicians, 1 was a study nurse, and 1 was a research assistant. RESULTS:The existing state of study feasibility estimation and the screening procedure were dominated by human communication and estimations from memory, although there were many possibilities for IT support. Success mostly depended on the experience and personal motivation of the clinical staff. Electronic support has been used but with little importance so far. Searches in ward-specific patient registers (databases) and searches in clinical information systems were reported. Furthermore, free-text searches in medical reports were mentioned. For potential future applications, a preference for either proactive or passive systems was not expressed. Most of the interviewees saw the potential for the improvement of the actual systems, but they were also largely satisfied with the outcomes of the current approach. Most of the interviewees were interested in learning more about the various ways in which IT could support and relieve them in their clinical routine. CONCLUSIONS:Overall, IT support currently plays a minor role in the screening step for clinical trials. The lack of IT usage and the estimations made from memory reported by all the participants might constrain cognitive resources, which might distract from clinical routine. We conclude that electronic support for the screening step for clinical trials is still a challenge and that education of the staff about the possibilities for electronic support in clinical trials is necessary.
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