Data management in clinical research: An overview

Towards equitable AI in oncology

Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with considerable potential to improve early cancer detection and risk assessment, and to enable more accurate personalized treatment recommendations. However, a notable imbalance exists in the distribution of the benefits of AI, which disproportionately favour those living in specific geographical locations and in specific populations. In this Perspective, we discuss the need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries. We also discuss some of the challenges and potential solutions in attaining equitable AI, including addressing the historically limited representation of diverse populations in existing clinical datasets and the use of inadequate clinical validation methods. Additionally, we focus on extant sources of inequity including the type of model approach (such as deep learning, and feature engineering-based methods), the implications of dataset curation strategies, the need for rigorous validation across a variety of populations and settings, and the risk of introducing contextual bias that comes with developing tools predominantly in high-income countries.

"Reality is frequently inaccurate" A case study examining the whens and whys of post-live database changes in a UK clinical trials unit *Douglas Adams

INTRODUCTION

Accurately estimating the costs of clinical trials is challenging. There is currently no reference class data to allow researchers to understand the potential costs associated with database change management in clinical trials.

METHODS

We used a case-based approach, summarising post-live changes in eleven clinical trial databases managed by Sheffield Clinical Trials Research Unit. We reviewed the database specifications for each trial and summarised the number of changes, change type, change category, and timing of changes. We pooled our experiences and made observations in relation to key themes.

RESULTS

Median total number of changes across the eleven trials was 71 (range 40-155) and median number of changes per study week was 0.48 (range 0.32-1.34). The most common change type was modification (median 39, range 20-90), followed by additions (median 32, range 18-55), then deletions (median 7, range 1-12). In our sample, changes were more common in the first half of the trial's lifespan, regardless of its overall duration. Trials which saw continuous changes seemed more likely to be external pilots or trials in areas where the trial team was either less experienced overall or within the particular therapeutic area.

CONCLUSIONS

Researchers should plan trials with the expectation that clinical trial databases will require changes within the life of the trial, particularly in the early stages or with a less experienced trial team. More research is required to understand potential differences between clinical trial units and database types.

A Practical Guide to Clinical Studies in Veterinary Oncology

This article explains the authors' experiences about opportunities, perspectives, and considerations required to initiate clinical studies in a veterinary oncology practice. These details include the infrastructure required for appropriate study training for all staff. Negotiation of scope of work and fees for service with study sponsors is also discussed. Finally, although generally similar, the article also describes management of clinical studies in academic and private practice settings.

Quality Management in Clinical and Public Health Research: A Panacea for Minimising and Eliminating Protocol Deviations in Research Operations

A quality management system for clinical and public health research operations is indispensable because it ensures the integrity and reliability of research outcomes. By implementing a robust quality management practice in research implementation and operation, research teams can uphold the highest standard of research conduct, thereby enhancing the credibility and trustworthiness of research findings. This paper elucidates the significance and role of a quality management system in clinical and public health research operations and its efficacy in minimising and eliminating protocol deviations and highlights the key steps in setting up a quality management system for research operations.

REDCapDM: An R package with a set of data management tools for a REDCap project

BACKGROUND

Research Electronic Data CAPture (REDCap) is a web application for creating and managing online surveys and databases. Clinical data management is an essential process before performing any statistical analysis to ensure the quality and reliability of study information. Processing REDCap data in R can be complex and often benefits from automation. While there are several R packages available for specific tasks, none offer an expansive approach to data management.

RESULTS

The REDCapDM is an R package for accessing and managing REDCap data. It imports data from REDCap to R using either an API connection or the files in R format exported directly from REDCap. It has several functions for data processing and transformation, and it helps to generate and manage queries to clarify or resolve discrepancies found in the data.

CONCLUSION

The REDCapDM package is a valuable tool for data scientists and clinical data managers who use REDCap and R. It assists in tasks such as importing, processing, and quality-checking data from their research studies.

A Simple Guide to Randomized Controlled Trials

Randomized controlled trials represent the cornerstone for the regulatory approval of drugs and evidence-based medicine and policy. Compared with observational studies random assignment of participants to each study arm guarantees an equal distribution of potential confounders thus achieving impartiality in the evaluation of between group differences and allowing for causal inferences to be drawn. These complex and costly medical experiments are tightly regulated and require substantial planning with great attention to several methodological aspects ranging from allocation concealment and blinding to sample size estimation, statistical analysis, and handling of protocol deviations. This brief guide offers useful insights into the design, conduct, and interpretation of clinical trial findings for beginners.

Considerations for establishing and maintaining international research collaboration: the example of chemotherapy-induced peripheral neurotoxicity (CIPN)-a white paper

PURPOSE

This white paper provides guidance regarding the process for establishing and maintaining international collaborations to conduct oncology/neurology-focused chemotherapy-induced peripheral neurotoxicity (CIPN) research.

METHODS

An international multidisciplinary group of CIPN scientists, clinicians, research administrators, and legal experts have pooled their collective knowledge regarding recommendations for establishing and maintaining international collaboration to foster advancement of CIPN science.

RESULTS

Experts provide recommendations in 10 categories: (1) preclinical and (2) clinical research collaboration; (3) collaborators and consortiums; (4) communication; (5) funding; (6) international regulatory standards; (7) staff training; (8) data management, quality control, and data sharing; (9) dissemination across disciplines and countries; and (10) additional recommendations about feasibility, policy, and mentorship.

CONCLUSION

Recommendations to establish and maintain international CIPN research collaboration will promote the inclusion of more diverse research participants, increasing consideration of cultural and genetic factors that are essential to inform innovative precision medicine interventions and propel scientific discovery to benefit cancer survivors worldwide.

RELEVANCE TO INFORM RESEARCH POLICY

Our suggested guidelines for establishing and maintaining international collaborations to conduct oncology/neurology-focused chemotherapy-induced peripheral neurotoxicity (CIPN) research set forth a challenge to multinational science, clinical, and policy leaders to (1) develop simple, streamlined research designs; (2) address logistical barriers; (3) simplify and standardize regulatory requirements across countries; (4) increase funding to support international collaboration; and (5) foster faculty mentorship.

Data Management 101 for drug developers: A peek behind the curtain

In drug development a frequently used phrase is "data-driven". Just as high-test gas fuels a car, so drug development "runs on" high-quality data; hence, good data management practices, which involve case report form design, data entry, data capture, data validation, medical coding, database closure, and database locking, are critically important. This review covers the essentials of clinical data management (CDM) for the United States. It is intended to demystify CDM, which means nothing more esoteric than the collection, organization, maintenance, and analysis of data for clinical trials. The review is written with those who are new to drug development in mind and assumes only a passing familiarity with the terms and concepts that are introduced. However, its relevance may also extend to experienced professionals that feel the need to brush up on the basics. For added color and context, the review includes real-world examples with RRx-001, a new molecular entity in phase III and with fast-track status in head and neck cancer, and AdAPT-001, an oncolytic adenovirus armed with a transforming growth factor-beta (TGF-β) trap in a phase I/II clinical trial with which the authors, as employees of the biopharmaceutical company, EpicentRx, are closely involved. An alphabetized glossary of key terms and acronyms used throughout this review is also included for easy reference.

Performance of four centralized statistical monitoring methods for early detection of an atypical center in a multicenter study

Background

Ensuring the quality of data is essential for the credibility of a multicenter clinical trial. Centralized Statistical Monitoring (CSM) of data allows the detection of a center in which the distribution of a specific variable is atypical compared to other centers. The ideal CSM method should allow early detection of problem and therefore involve the fewest possible participants.

Methods

We simulated clinical trials and compared the performance of four CSM methods (Student, Hatayama, Desmet, Distance) to detect whether the distribution of a quantitative variable was atypical in one center in relation to the others, with different numbers of participants and different mean deviation amplitudes.

Results

The Student and Hatayama methods had good sensitivity but poor specificity, which disqualifies them for practical use in CSM. The Desmet and Distance methods had very high specificity for detecting all the mean deviations tested (including small values) but low sensitivity with mean deviations less than 50%.

Conclusion

Although the Student and Hatayama methods are more sensitive, their low specificity would lead to too many alerts being triggered, which would result in additional unnecessary control work to ensure data quality. The Desmet and Distance methods have low sensitivity when the deviation from the mean is low, suggesting that the CSM should be used alongside other conventional monitoring procedures rather than replacing them. However, they have excellent specificity, which suggests they can be applied routinely, since using them takes up no time at central level and does not cause any unnecessary workload in investigating centers.

The Minimum Data Set for Rare Diseases: Systematic Review

BACKGROUND

The minimum data set (MDS) is a collection of data elements to be grouped using a standard approach to allow the use of data for clinical and research purposes. Health data are typically voluminous, complex, and sometimes too ambiguous to generate indicators that can provide knowledge and information on health. This complexity extends further to the rare disease (RD) domain. MDSs are essential for health surveillance as they help provide services and generate recommended population indicators. There is a bottleneck in international literature that reveals a global problem with data collection, recording, and structuring in RD.

OBJECTIVE

This study aimed to identify and analyze the MDSs used for RD in health care networks worldwide and compare them with World Health Organization (WHO) guidelines.

METHODS

The population, concept, and context methodology proposed by the Joanna Briggs Institute was used to define the research question of this systematic review. A total of 4 databases were reviewed, and all the processes were reported using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. The data elements were analyzed, extracted, and organized into 10 categories according to WHO digital health guidelines. The quality assessment used the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist.

RESULTS

We included 20 studies in our review, 70% (n=14) of which focused on a specific health domain and 30% (n=6) of which referred to RD in general. WHO recommends that health systems and networks use standard terminology to exchange data, information, knowledge, and intelligence in health. However, there was a lack of terminological standardization of the concepts in MDSs. Moreover, the selected studies did not follow the same standard structure for classifying the data from their MDSs. All studies presented MDSs with limitations or restrictions because they covered only a specific RD, or their scope of application was restricted to a specific context or geographic region. Data science methods and clinical experience were used to design, structure, and recommend a fundamental global MDS for RD patient records in health care networks.

CONCLUSIONS

Our study highlights the difficulties in standardizing and categorizing findings from MDSs for RD because of the varying structures used in different studies. The fundamental RD MDS designed in this study comprehensively covers the data needs in the clinical and management sectors. These results can help public policy makers support other aspects of their policies. We highlight the potential of our results to help strategic decisions related to RD.

TRIAL REGISTRATION

PROSPERO CRD42021221593; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221593.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1016/j.procs.2021.12.034.

REDbox: a comprehensive semantic framework for data collection and management in tuberculosis research

Clinical research outcomes depend on the correct definition of the research protocol, the data collection strategy, and the data management plan. Furthermore, researchers often need to work within challenging contexts, as is the case in tuberculosis services, where human and technological resources for research may be scarce. Electronic Data Capture Systems mitigate such risks and enable a reliable environment to conduct health research and promote result dissemination and data reusability. The proposed solution is based on needs pinpointed by researchers, considering the need for an accommodating solution to conduct research in low-resource environments. The REDbox framework was developed to facilitate data collection, management, sharing, and availability in tuberculosis research and improve the user experience through user-friendly, web-based tools. REDbox combines elements of the REDCap and KoBoToolbox electronic data capture systems and semantics to deliver new valuable tools that meet the needs of tuberculosis researchers in Brazil. The framework was implemented in five cross-institutional, nationwide projects to evaluate the users' perceptions of the system's usefulness and the information and user experience. Seventeen responses (representing 40% of active users) to an anonymous survey distributed to active users indicated that REDbox was perceived to be helpful for the particular audience of researchers and health professionals. The relevance of this article lies in the innovative approach to supporting tuberculosis research by combining existing technologies and tailoring supporting features.

Data management system for diabetes clinical trials: a pre-post evaluation study

BACKGROUND

Data management system for diabetes clinical trials is used to support clinical data management processes. The purpose of this study was to evaluate the quality and usability of this system from the users' perspectives.

METHODS

This study was conducted in 2020, and the pre-post evaluation method was used to examine the quality and usability of the designed system. Initially, a questionnaire was designed and distributed among the researchers who were involved in the diabetes clinical trials (n = 30) to investigate their expectations. Then, the researchers were asked to use the system and explain their perspectives about it by completing two questionnaires.

RESULTS

There was no statistically significant differences between the users' perspectives about the information quality, service quality, achievements, and communication before and after using the system. However, in terms of the system quality (P = 0.042) and users' autonomy (P = 0.026), the users' expectations were greater than the system performance. The system usability was at a good level based on the users' opinions.

CONCLUSION

It seems that the designed system largely met the users' expectations in most areas. However, the system quality and users' autonomy need further attentions. In addition, the system should be used in multicenter trials and re-evaluated by a larger group of users.

What does a department need to get involved in clinical trials?

Clinical trials are conducted to evaluate the usefulness of a drug, medical treatment, surgical intervention, radiological procedure, behavioral intervention, or preventive health care strategy. Clinical trials are challenging, time-consuming, and need careful planning and execution. There are certain requirements for an academic unit to be ready for conducting quality research, especially clinical trials. These include logistical concerns, infrastructure, appropriately trained human resources, conformity with ethical compliances and regulatory compliances as per the laws governing the respective geographical regions. Infrastructure requirements include research division space, robust data archival system, archival of imaging data, laboratory services, information technology division and facilities for the appropriate consenting process. Human resources such as principal investigators, data managers, clinical research assistants and biostatisticians are needed to execute the clinical trial as per the study design. Adherence to ethical and regulatory standards; during the conduct of the clinical trial are imperative. Strict compliance with prevailing governing laws in the country is a prerequisite for a department to get involved in clinical trials. The department also needs to put in place an institutional ethics committee; the composition of which is often governed by regulatory laws. The ethical aspects of the trial undergo a formal scrutiny by an Institute ethics committee that ensures quality care to the patient, and safeguards the patient's rights and privileges. The academic unit of any department forms the core for possible research and pursuit of clinical trials. A department with rich clinical experience and expertise in the filed aids in the execution of quality research. The academic department must consider grant writing and multiple center collaborative research projects in due course of time to further enhance the quality of clinical trials and research output.

Promoting the quality and transparency of health research in China

BACKGROUND AND OBJECTIVES

To review the efforts and progress in promoting quality and transparency of health research in China and to discuss how to improve.

METHODS

We focused on three different types of health research: clinical trials, systematic reviews, and clinical practice guidelines, and summarized China's progress from their registration, implementation, and reporting stages.

RESULTS

In the last 3 decades, China's major works include the following: 1) For registration, the trials and CPGs registries have been set up, and released policies to mandate registration and submission of protocols for trials. 2) For implementation, multiple clinical research and EBM centers have been established. The ResMan platform for data management has been created and required researchers to share results data. 3) For reporting, the Chinese EQUATOR Center has been set up. Translations and extensions of the CONSORT, PRISMA, and RIGHT have been published. 4) For traditional Chinese medicine (TCM), specific guidelines for the registration and reporting of TCM research have been developed.

CONCLUSION

China has made progress in promoting research quality and transparency, especially in research registration and reporting. Nevertheless, more can be done in data management and sharing for research implementation, and good publication practices in TCM.

Comparison of 0.12% Chlorhexidine and a New Bone Bioactive Liquid, BBL, in Mouthwash for Oral Wound Healing: A Randomized, Double Blind Clinical Human Trial

Following surgery, healing within the oral cavity occurs in a hostile environment, and proper oral care and hygiene are required to accelerate recovery. The aim of the current study is to investigate and compare the bioreactivity characteristics of mouthwashes based on either chlorhexidine (CHX) or a novel bone bioactive liquid (BBL) in terms of oral healing within seven days application post-surgery. A randomized, double blind clinical trial was conducted in 81 patients, wherein the mouthwashes were applied twice a day for a period of 7 days. The visual analog scale (VAS) protocol was applied to determine pain index scores. Early wound healing index (EHI) score was determined for evaluating oral cavity healing progress. No adverse effects were observed using the mouthwashes, but CHX application resulted in stained teeth. Applications of both CHX and BBL were sufficient to reduce pain over a period of 7 days. However, the BBL group demonstrated a statistically significant reduction in VAS scores starting on day 4. The EHI scores were significantly higher in the BBL group compared with the CHX group, independent of tooth location. No differences in either VAS or EHI scores due to gender were observed. Compared with the commercially available CHX mouthwash, application of the BBL mouthwash reduced pain and accelerated oral cavity healing to a greater extent, suggesting it effectively improves the oral cavity microenvironment at the wound site in mediating soft tissue regeneration.

Data management in diabetes clinical trials: a qualitative study

Background

Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in clinical trials. Lack of appropriate planning for data management in clinical trials may negatively influence achieving the desired results. The aim of this study was to explore data management processes in diabetes clinical trials in three research institutes in Iran.

Method

This was a qualitative study conducted in 2019. In this study, data were collected through in-depth semi-structured interviews with 16 researchers in three endocrinology and metabolism research institutes. To analyze data, the method of thematic analysis was used.

Results

The five themes that emerged from data analysis included (1) clinical trial data collection, (2) technologies used in data management, (3) data security and confidentiality management, (4) data quality management, and (5) data management standards. In general, the findings indicated that no clear and standard process was used for data management in diabetes clinical trials, and each research center executed its own methods and processes.

Conclusion

According to the results, the common methods of data management in diabetes clinical trials included a set of paper-based processes. It seems that using information technology can help facilitate data management processes in a variety of clinical trials, including diabetes clinical trials.

A Clinical Data Management System for Diabetes Clinical Trials

Background

The use of novel medications and methods to prevent, diagnose, treat, and manage diabetes requires confirmation of safety and efficacy in a well-designed study prior to widespread adoption. Diabetes clinical trials are the studies that examine these issues. The aim of the present study was to develop a web-based system for data management in diabetes clinical trials.

Methods

The present research was a mixed-methods study conducted in 2019. To identify the required data elements and functions to develop the system, 60 researchers completed a questionnaire. The designed system was evaluated using two methods. The usability of the system was initially evaluated by a group of researchers (n = 6) using the think-aloud method, and after system improvement, the system functions were evaluated by other researchers (n = 30) using a questionnaire.

Results

The main data elements which were required to develop a case report form included “study data,” “participant's personal data,” and “clinical data.” The functional requirements of the system were “managing the study,” “creating case report forms,” “data management,” “data quality control,” and “data security and confidentiality.” After using the system, researchers rated the system functions at a “good” level (6.3 ± 0.73) on a seven-point Likert scale.

Conclusion

Given the complexity of the data management processes in diabetes clinical trials and the widespread use of information technologies in research, the use of clinical data management systems in diabetes clinical trials seems inevitable. The system developed in the current study can facilitate and improve the process of creating and managing case report forms as well as collecting data in diabetes clinical trials.

Informing training needs for the revised certified clinical data manager (CCDMTM) exam: analysis of results from the previous exam

Objective

To inform training needs for the revised Certified Clinical Data Manager (CCDM^TM) Exam.

Introduction

Clinical data managers hold the responsibility for processing the data on which research conclusions and regulatory decisions are based, highlighting the importance of applying effective data management practices. The use of practice standards such as the Good Clinical Data Management Practices increases confidence in data, emphasizing that the study conclusions likely hold much more weight when utilizing standard practices.

Methods

A quantitative, descriptive study, and application of classic test theory was undertaken to analyze past data from the CCDM^TM Exam to identify potential training needs. Data across 952 sequential exam attempts were pooled for analysis.

Results

Competency domain-level analysis identified training needs in 4 areas: design tasks; data processing tasks; programming tasks; and coordination and management tasks.

Conclusions

Analysis of past CCDM^TM Exam results using classic test theory identified training needs reflective of exam takers. Training in the identified areas could benefit CCDM^TM Exam takers and improve their ability to apply effective data management practices. While this may not be reflective of individual or organizational needs, recommendations for assessing individual and organizational training needs are provided.

This study analyzed past data from the Certified Clinical Data Manager^TM Exam (CCDM^TM) to identify potential training needs. Data across 952 sequential exam attempts were pooled for analysis. The study identified training needs in 4 areas of clinical data management: design tasks; data processing tasks; programming tasks; and coordination and management tasks. Training in the 4 identified areas could benefit CCDM^TM Exam takers and improve their ability to apply effective data management practices.

DaT3M: A Data Tracker for Multi-faceted Management of Multi-site Clinical Research Data Submission, Curation, Master Inventorying, and Sharing

Managing research data is an important and challenging aspect of clinical studies, especially for multi-site collaboratives. To address this challenge, we designed, developed and deployed a multi-faceted, multi-level interactive data tracker (DaT3M) for multi-site clinical research data submission, curation, master inventorying, and sharing. Components of DaT3M include data overview, data portal, data status panel, data query engine, and data downloader. DaT3M managed clinical research data for the Center for SUDEP Research (CSR). The CSR instance of DaT3M includes 2,743 subjects from seven data contributing institutions, 7 data modalities and 10,678 data components: 3,398 Epilepsy Monitoring Unit reports, 3,440 electroencephalography recordings, 629 MRI imaging datasets, 177 bio-chemistry datasets, 722 DNA datasets, 2,289 follow-up forms, and 30 SUDEP forms. Preliminary, structured, one-on-one usability evaluations were performed with 7 researchers from four institutions. System Usability Score reached 85.3, showing that DaT3M has achieved high levels of user satisfaction based on our pilot evaluation.

An Open-Source, Standard-Compliant, and Mobile Electronic Data Capture System for Medical Research (OpenEDC): Design and Evaluation Study

BACKGROUND

Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages.

OBJECTIVE

The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects.

METHODS

We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users.

RESULTS

We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license.

CONCLUSIONS

Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.

Digital Tumor Board Solutions Have Significant Impact on Case Preparation

PURPOSE

Multidisciplinary tumor boards (TBs) are the gold standard for decision-making in cancer care. Variability in preparation, conduction, and impact is widely reported. The benefit of digital technologies to support TBs is unknown. This study evaluated the impact of the NAVIFY Tumor Board solution (NTB) on TB preparation time across multiple user groups in 4 cancer categories: breast, GI, head and neck (ie, ear, nose, and throat, or ENT), and hematopathology.

METHODS

This prospective study evaluated TB preparation time in multiple phases pre- and post-NTB implementation at an academic health care center. TB preparation times were recorded for multiple weeks using a digital time tracker.

RESULTS

Preparation times for 59 breast, 61 GI, 36 ENT, and 71 hematopathology cancer TBs comparing a pre-NTB phase to 3 phases of NTB implementation were evaluated between February 2018 and July 2019. NTB resulted in significant reductions in overall preparation time (30%) across 3 TBs pre-NTB compared with the final post-NTB implementation phase. In the breast TB, NTB reduced overall preparation time by 28%, with a 76% decrease in standard deviation (SD). In the GI TB, a 23% reduction in average preparation time was observed for all users, with a 48% decrease in SD. In the ENT TB, a 33% reduction in average preparation time was observed for all users, with a 73% decrease in SD. The hematopathology TB, which was the cocreation partner and initial adopter of the solution, showed variable results.

CONCLUSION

This study showed a significant impact of a digital solution on time preparation for TBs across multiple users and different TBs, reflecting the generalizability of the NTB. Adoption of such a solution could improve the efficiency of TBs and have a direct economic impact on hospitals.

Implementing an automated monitoring process in a digital, longitudinal observational cohort study

Background

Clinical data collection requires correct and complete data sets in order to perform correct statistical analysis and draw valid conclusions. While in randomized clinical trials much effort concentrates on data monitoring, this is rarely the case in observational studies- due to high numbers of cases and often-restricted resources. We have developed a valid and cost-effective monitoring tool, which can substantially contribute to an increased data quality in observational research.

Methods

An automated digital monitoring system for cohort studies developed by the German Rheumatism Research Centre (DRFZ) was tested within the disease register RABBIT-SpA, a longitudinal observational study including patients with axial spondyloarthritis and psoriatic arthritis. Physicians and patients complete electronic case report forms (eCRF) twice a year for up to 10 years. Automatic plausibility checks were implemented to verify all data after entry into the eCRF. To identify conflicts that cannot be found by this approach, all possible conflicts were compiled into a catalog. This “conflict catalog” was used to create queries, which are displayed as part of the eCRF. The proportion of queried eCRFs and responses were analyzed by descriptive methods. For the analysis of responses, the type of conflict was assigned to either a single conflict only (affecting individual items) or a conflict that required the entire eCRF to be queried.

Results

Data from 1883 patients was analyzed. A total of n = 3145 eCRFs submitted between baseline (T0) and T3 (12 months) had conflicts (40–64%). Fifty-six to 100% of the queries regarding eCRFs that were completely missing were answered. A mean of 1.4 to 2.4 single conflicts occurred per eCRF, of which 59–69% were answered. The most common missing values were CRP, ESR, Schober’s test, data on systemic glucocorticoid therapy, and presence of enthesitis.

Conclusion

Providing high data quality in large observational cohort studies is a major challenge, which requires careful monitoring. An automated monitoring process was successfully implemented and well accepted by the study centers. Two thirds of the queries were answered with new data. While conventional manual monitoring is resource-intensive and may itself create new sources of errors, automated processes are a convenient way to augment data quality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02563-2.

A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy

The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity "REQUITE". We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers' attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.

A digital tumor board solution impacts case discussion time and postponement of cases in tumor boards

Multidisciplinary tumor boards (TBs) is an integral part of cancer care. Emerging evidence shows that effective TB implementation is crucial. It remains largely unknown how digital solutions can assist effective TB conduction. This study aimed to evaluate the impact of a digital solution on case discussion during TB meetings in four cancer types: Breast, Gastrointestinal (GI), Ear, Nose & Throat (ENT), and Hematopathology. A prospective study was performed to evaluate case discussion time during TB meetings pre- and post-solution implementation, at an US academic healthcare cancer center. Data were recorded by a Nurse Navigator for each case during TB meetings. Case discussion times were recorded for 2312 patients, at a total of 286 TB meetings. Significant decreases were observed in the average case discussion time for the breast and GI TBs. We observed a trend for reduction in discussion time variance for all TBs, suggesting the potential of the digital solution to standardize case discussion via provision of uniform case presentation and data access. Postponement rate decreased from 23 to 10% for ENT TB. This study demonstrated that the digital solution enhanced effective TB implementation, with heterogeneity across cancer types.

Inventory of Youth Adaptation to Loss (IYAL): Psychometric Testing of a New Instrument for Bereaved Youth to Assess Social Support and Coping

The Inventory of Youth Adaptation to Loss (IYAL) was developed to understand the feelings and social supports experienced by bereaved youth to develop an evidence-base for bereavement interventions. The sample included 400 youth ages 9-17, permitting robust psychometric testing of the IYAL. To evaluate the reliability of the IYAL, the sample framework was national in scope, encompassed a range of different types of youth bereavement programs, and purposively sought diversity in the sample. Exploratory factor analysis identified five factors with distinct sub-scales indicating that the IYAL is a valid and reliable assessment instrument of youth coping with grief and their social support relationships.

Key components and IT assistance of participant management in clinical research: a scoping review

Objectives

Managing participants and their data are fundamental for the success of a clinical trial. Our review identifies and describes processes that deal with management of trial participants and highlights information technology (IT) assistance for clinical research in the context of participant management.

Methods

A scoping literature review design, based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement, was used to identify literature on trial participant-related proceedings, work procedures, or workflows, and assisting electronic systems.

Results

The literature search identified 1329 articles of which 111 were included for analysis. Participant-related procedures were categorized into 4 major trial processes: recruitment, obtaining informed consent, managing identities, and managing administrative data. Our results demonstrated that management of trial participants is considered in nearly every step of clinical trials, and that IT was successfully introduced to all participant-related areas of a clinical trial to facilitate processes.

Discussion

There is no precise definition of participant management, so a broad search strategy was necessary, resulting in a high number of articles that had to be excluded. Nevertheless, this review provides a comprehensive overview of participant management-related components, which was lacking so far. The review contributes to a better understanding of how computer-assisted management of participants in clinical trials is possible.

A protocol for a systematic review investigating the factors influencing the statistical planning, design, conduct, analysis and reporting of trials

Trials can be defined as prospective human research studies to test the effectiveness and safety of interventions, such as medications, surgeries, medical devices and other interventions for the management of patient care. Statistics is an important and powerful tool in trials. Inappropriately designed trials and/or inappropriate statistical analysis produce unreliable results, with limited clinical use. The aim of this systematic literature review is to identify, describe and synthesise factors contributing to or influencing the statistical planning, design, conduct, analysis and reporting of trials. This protocol will describe the methodological approach taken for the following: conducting a systematic and comprehensive search for relevant articles, applying eligibility criteria for the inclusion of such articles, extracting data and information, appraising the quality of the articles, and thematically synthesizing the data to illuminate the key factors influencing statistical aspects of trials.

A protocol for a systematic review of clinical practice guidelines for recurrent miscarriage

Recurrent miscarriage (RM) was recently re-defined by the European Society of Human Reproduction and Embryology (ESHRE) as the loss of two or more consecutive pregnancies. Before this, and indeed still in some countries, RM was defined as three or more consecutive pregnancy losses. While the incidence of RM depends on the definition employed and population studied, it is generally accepted to affect 1-6% of women of reproductive age. Clinical practice guidelines (CPGs) for RM have been published by some professional organisations. While there are CPGs on miscarriage in Ireland, there are none concerning RM specifically. The aim of this systematic review is to identify, appraise and describe published CPGs for the management, investigation and/or follow-up of RM within high-income countries. Electronic databases (MEDLINE (Ovid®; 1946), Embase® (Elsevier; 1980), CINAHL Complete (EBSCOhost; 1994), Web of Science™ (Thomson Reuters), Scopus (Elsevier; 2004), and Open Grey (INIST-CNRS; 2011)), selected guideline repositories, and the websites of professional societies will be searched to identify CPGs, published within the last 20 years, for potential inclusion. Two reviewers will review abstracts and full texts independently against the eligibility criteria. Characteristics and recommendations of included CPGs will be extracted by one reviewer and double-checked by another. Two reviewers will use the Appraisal of Guidelines for Research and Evaluation version 2 (AGREE II) instrument independently to assess the quality of the included CPGs. Narrative synthesis will be conducted to appraise and compare CPGs and their recommendations or guidance therein. The identification, appraisal and description of published CPGs in other high-income countries will be a valuable first step in informing efforts to promote the optimisation and standardisation of RM care.

A protocol for a systematic review investigating the factors influencing the statistical planning, design, conduct, analysis and reporting of clinical trials

Clinical trials can be defined as prospective human research studies to test the effectiveness and safety of interventions, such as medications, surgeries, medical devices and other interventions for the management of patient care. Statistics is an important and powerful tool in clinical trials. Inappropriately designed trials and/or inappropriate statistical analysis produce unreliable results, with limited clinical use. The aim of this systematic literature review is to identify, describe and synthesise factors contributing to or influencing the statistical planning, design, conduct, analysis and reporting of clinical trials. This protocol will describe the methodological approach taken for the following: conducting a systematic and comprehensive search for relevant articles, applying eligibility criteria for the inclusion of such articles, extracting data and information, appraising the quality of the articles, and thematically synthesizing the data to illuminate the key factors influencing statistical aspects of clinical trials.

High throughput image labeling on chest computed tomography by deep learning

When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy > 99 % on test set across different tasks using a research database from multicenter clinical trials.

Ten simple rules for providing effective bioinformatics research support

Life scientists are increasingly turning to high-throughput sequencing technologies in their research programs, owing to the enormous potential of these methods. In a parallel manner, the number of core facilities that provide bioinformatics support are also increasing. Notably, the generation of complex large datasets has necessitated the development of bioinformatics support core facilities that aid laboratory scientists with cost-effective and efficient data management, analysis, and interpretation. In this article, we address the challenges-related to communication, good laboratory practice, and data handling-that may be encountered in core support facilities when providing bioinformatics support, drawing on our own experiences working as support bioinformaticians on multidisciplinary research projects. Most importantly, the article proposes a list of guidelines that outline how these challenges can be preemptively avoided and effectively managed to increase the value of outputs to the end user, covering the entire research project lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage). In addition, we highlight the importance of clear and transparent communication, comprehensive preparation, appropriate handling of samples and data using monitoring systems, and the employment of appropriate tools and standard operating procedures to provide effective bioinformatics support.

Critical Challenges to the Management of Clinical Trial Imaging: Recommendations for the Conduct of Imaging at Investigational Sites

RATIONALE AND OBJECTIVES

Participation in clinical research can be both highly rewarding and logistically demanding. As highlighted by recent Food and Drug Administration guidance, imaging has become an integral part of this research. The unique technical and administrative aspects of clinical trial imaging may differ substantially from those of standard-of-care imaging and thus burden the established clinical infrastructure at investigational sites. Failure to comply with requirements can lead to unusable data, repeat imaging, or the removal of patients from the trial. It is therefore imperative that all stakeholders address these challenges to engage in clinical research successfully.

MATERIALS AND METHODS

The authors' experiences in managing clinical trial imaging requirements at their institution were used to identify common challenges. The impact of these challenges was assessed from an operational perspective.

RESULTS

Although contract research organizations attempt to minimize these challenges, their efforts are necessarily limited and insufficient, and there is a lack of infrastructure available at investigational sites to address these issues. As such, recommendations are proposed for addressing these challenges at institutional and industry levels.

CONCLUSION

The challenges associated with clinical trial imaging require an investment of resources from all stakeholders. Investigational sites must confront these challenges to satisfy trial requirements effectively, maintain a superior level of patient care, and guarantee trial integrity. Similarly, sponsors must acknowledge the burden of clinical trial imaging and support the development of the necessary local infrastructure. The implementation of the recommendations described here will improve the conduct of clinical trial imaging.

Clinical Trial Data Management in Environmental Health Tailored for an African Setting

Clinical trial data management tools are widely available—some free to access and others relatively expensive, particularly for low- and middle-income countries. Such tools also do not always permit adaptation for local conditions nor include options to capture environmental and meteorological data. In the context of climate change and pressing environmental health threats, more studies that aim to assess the impacts of environmental change on public health are being carried out. Here, using freely available software, we tailor-made a clinical trial data management tool that managed all aspects of an intervention-based clinical trial to assess the impact of personal solar ultraviolet radiation exposure on vaccine effectiveness. Data captured and associated procedures included patient data, scheduling, reporting, analysis and data management. Moreover, patient enrolment, recruitment, follow-up and decision-making in response to patient data were managed. Given the multidisciplinary study approach, the tool also managed all environmental and meteorological data for the rural African study site. Application of the tool ensured efficient communication between rural sites, a relatively high overall participant response rate (87%) and minimal loss to follow-up. This study suggests that it is possible to tailor-make a clinical trial data management tool for environmental and public health studies.

Design of a "Lean" Case Report Form for Heart Failure Therapeutic Development

The development of treatments for heart failure (HF) is challenged by burdensome clinical trials. Reducing the need for extensive data collection and increasing opportunities for data compatibility between trials may improve efficiency and reduce resource burden. The Heart Failure Collaboratory (HFC) multi-stakeholder consortium sought to create a lean case report form (CRF) for use in HF clinical trials evaluating cardiac devices. The HFC convened patients, clinicians, clinical researchers, the U.S. Food and Drug Administration (FDA), payers, industry partners, and statisticians to create a consensus core CRF. Eight recent clinical trial CRFs for the treatment of HF from 6 industry partners were analyzed. All CRF elements were systematically reviewed. Those elements deemed critical for data collection in HF clinical trials were used to construct the final, harmonized CRF. The original CRFs included 176 distinct data items covering demographics, vital signs, physical examination, medical history, laboratory and imaging testing, device therapy, medications, functional and quality of life assessment, and outcome events. The resulting, minimally inclusive CRF device contains 75 baseline data items and 6 events, with separate modular additions that can be used depending on the additional detail required for a particular intervention. The consensus electronic form is now freely available for use in clinical trials. Creation of a core CRF is important to improve clinical trial efficiency in HF device development in the United States. This living document intends to reduce clinical trial administrative burden, increase evidence integrity, and improve comparability of clinical data between trials.

A Multidisciplinary Evaluation of Barriers to Enrolling Cancer Patients into Early Phase Clinical Trials: Challenges and Patient-centric Recommendations

Introduction: Early phase clinical trials are the first clinical research step to bringing new cancer therapeutics to patients. At this stage, a new drug's safety, dosing, and scheduling profiles are established as the main endpoints. However, excellent responses due to biomarker-guided and immune checkpoint trials in early phase have resulted in direct approvals of new anti-cancer drugs. Despite doubling of the success rate of new drug approvals, many barriers exist to expeditiously bring active new drugs to the clinic. Areas covered: This review covers roles of members of the early phase program and the challenges they face in enrolling advanced cancer patients to trials. Practical solutions are provided from the perspective of the investigators, regulatory, investigational pharmacy, research nurses, clinical research coordinators, budgets, contracts, and data management. Expert opinion: We are witnessing a burgeoning era in drug development with rapid approval of efficacious drugs. This is achieved by a strong collaboration between investigators, academic institutions, pharmaceutical sponsors, scientists, Food and Drug Administration (FDA), and community practices. Herein, we discuss some of the challenges faced by early phase clinical trials programs and discuss methods of improvement.

Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing-Driven Medical Information Extraction System: Quantitative Field Research

BACKGROUND

The growing interest in observational trials using patient data from electronic medical records poses challenges to both efficiency and quality of clinical data collection and management. Even with the help of electronic data capture systems and electronic case report forms (eCRFs), the manual data entry process followed by chart review is still time consuming.

OBJECTIVE

To facilitate the data entry process, we developed a natural language processing-driven medical information extraction system (NLP-MIES) based on the i2b2 reference standard. We aimed to evaluate whether the NLP-MIES-based eCRF application could improve the accuracy and efficiency of the data entry process.

METHODS

We conducted a randomized and controlled field experiment, and 24 eligible participants were recruited (12 for the manual group and 12 for NLP-MIES-supported group). We simulated the real-world eCRF completion process using our system and compared the performance of data entry on two research topics, pediatric congenital heart disease and pneumonia.

RESULTS

For the congenital heart disease condition, the NLP-MIES-supported group increased accuracy by 15% (95% CI 4%-120%, P=.03) and reduced elapsed time by 33% (95% CI 22%-42%, P<.001) compared with the manual group. For the pneumonia condition, the NLP-MIES-supported group increased accuracy by 18% (95% CI 6%-32%, P=.008) and reduced elapsed time by 31% (95% CI 19%-41%, P<.001).

CONCLUSIONS

Our system could improve both the accuracy and efficiency of the data entry process.

Changing platforms without stopping the train: experiences of data management and data management systems when adapting platform protocols by adding and closing comparisons

Background

There is limited research and literature on the data management challenges encountered in multi-arm, multi-stage platform and umbrella protocols. These trial designs allow both (1) seamless addition of new research comparisons and (2) early stopping of accrual to individual comparisons that do not show sufficient activity. FOCUS4 (colorectal cancer) and STAMPEDE (prostate cancer), run from the Medical Research Council Clinical Trials Unit (CTU) at UCL, are two leading UK examples of clinical trials implementing adaptive platform protocol designs. To date, STAMPEDE has added five new research comparisons, closed two research comparisons following pre-planned interim analysis (lack of benefit), adapted the control arm following results from STAMPEDE and other relevant trials, and completed recruitment to six research comparisons. FOCUS4 has closed one research comparison following pre-planned interim analysis (lack of benefit) and added one new research comparison, with a number of further comparisons in the pipeline. We share our experiences from the operational aspects of running these adaptive trials, focusing on data management.

Methods

We held discussion groups with STAMPEDE and FOCUS4 CTU data management staff to identify data management challenges specific to adaptive platform protocols. We collated data on a number of case report form (CRF) changes, database amendments and database growth since each trial began.

Discussion

We found similar adaptive protocol-specific challenges in both trials. Adding comparisons to and removing them from open trials provides extra layers of complexity to CRF and database development. At the start of an adaptive trial, CRFs and databases must be designed to be flexible and scalable in order to cope with the continuous changes, ensuring future data requirements are considered where possible. When adding or stopping a comparison, the challenge is to incorporate new data requirements while ensuring data collection within ongoing comparisons is unaffected. Some changes may apply to all comparisons; others may be comparison-specific or applicable only to patients recruited during a specific time period. We discuss the advantages and disadvantages of the different approaches to CRF and database design we implemented in these trials, particularly in relation to use and maintenance of generic versus comparison-specific CRFs and databases. The work required to add or remove a comparison, including the development and testing of changes, updating of documentation, and training of sites, must be undertaken alongside data management of ongoing comparisons. Adequate resource is required for these competing data management tasks, especially in trials with long follow-up. A plan is needed for regular and pre-analysis data cleaning for multiple comparisons that could recruit at different rates and periods of time. Data-cleaning activities may need to be split and prioritised, especially if analyses for different comparisons overlap in time.

Conclusions

Adaptive trials offer an efficient model to run randomised controlled trials, but setting up and conducting the data management activities in these trials can be operationally challenging. Trialists and funders must plan for scalability in data collection and the resource required to cope with additional competing data management tasks.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3322-7) contains supplementary material, which is available to authorized users.

Efficacy and safety of gyejigachulbutang (Gui-Zhi-Jia-Shu-Fu-Tang, Keishikajutsubuto, TJ-18) for knee pain in patients with degenerative knee osteoarthritis: a randomized, placebo-controlled, patient and assessor blinded clinical trial

Background

Degenerative knee osteoarthritis is a leading cause of disability in the elderly. If patients do not respond to pharmacological or nonpharmacological intervention, total knee replacement surgery is recommended. However, owing to the contraindications and adverse effects of surgery, the need for a new treatment strategy is emerging. Traditional herbal medicine is a widely used intervention in east Asia to treat knee osteoarthritis. Gyejigachulbutang is one of the frequently prescribed herbal formulae. The aim of our study is to evaluate the efficacy and safety of gyejigachulbutang for knee osteoarthritis.

Methods

This study is a randomized, placebo-controlled, patient and assessor blinded, superiority clinical trial. A total of 80 patients with knee osteoarthritis will be enrolled. The participants will be randomly assigned to the gyejigachulbutang or placebo group in a 1:1 ratio in two Korean medical hospitals. Every participant will take gyejigachulbutang or placebo at a dose of 2.5 g three times a day for 4 weeks. Additional follow-up will be conducted 4 weeks after treatment completion. Any concomitant treatment to relive knee pain will not be allowed except for rescue medicine (acetaminophen). The primary outcome will be a comparison of the change in the visual analogue scale score for knee pain from baseline to visit 3 (week 4) for both the treatment and placebo groups. Secondary outcomes include clinical relevance, minimal clinically important difference, disability, quality of life, and safety.

Discussion

This protocol presents a research methodology for clinical trials of gyejigachulbutang for knee osteoarthritis. Various secondary outcomes make this trial more informative. Our trial will provide fundamental evidence for knee osteoarthritis management via herbal medicine treatment.

Trial registration

Clinical Research Information Service (CRIS), KCT0003024. Registered on 25 July 2018.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3234-6) contains supplementary material, which is available to authorized users.

Clinical Trial Data Management Software: A Review of the 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.

Adapting data management education to support clinical research projects in an academic medical center

Background

Librarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown.

Case Presentation

Leveraging my academic library's experience supporting CRDM through informationist grants and REDCap training in our medical center, I developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library's existing data support program. The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop.

Conclusions

The 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work. Attendees also articulated how they would implement the main competencies they learned from the workshop into their work. For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing.

A Review of Clinical Data Management Systems Used in Clinical Trials

BACKGROUND

A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems.

METHODS

This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials.

RESULTS

The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance.

CONCLUSION

Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.

A cross-source, system-agnostic solution for clinical data review

Assembly of complete and error-free clinical trial data sets for statistical analysis and regulatory submission requires extensive effort and communication among investigational sites, central laboratories, pharmaceutical sponsors, contract research organizations and other entities. Traditionally, this data is captured, cleaned and reconciled through multiple disjointed systems and processes, which is resource intensive and error prone. Here, we introduce a new system for clinical data review that helps data managers identify missing, erroneous and inconsistent data and manage queries in a unified, system-agnostic and efficient way. Our solution enables timely and integrated access to all study data regardless of source, facilitates the review of validation and discrepancy checks and the management of the resulting queries, tracks the status of page review, verification and locking activities, monitors subject data cleanliness and readiness for database lock and provides extensive configuration options to meet any study's needs, automation for regular updates and fit-for-purpose user interfaces for global oversight and problem detection.

MAV-clic: management, analysis, and visualization of clinical data

Objectives

Develop a multifunctional analytics platform for efficient management and analysis of healthcare data.

Materials and Methods

Management, Analysis, and Visualization of Clinical Data (MAV-clic) is a Health Insurance Portability and Accountability Act of 1996 (HIPAA)-compliant framework based on the Butterfly Model. MAV-clic extracts, cleanses, and encrypts data then restructures and aggregates data in a deidentified format. A graphical user interface allows query, analysis, and visualization of clinical data.

Results

MAV-clic manages healthcare data for over 800 000 subjects at UConn Health. Three analytic capabilities of MAV-clic include: creating cohorts based on specific criteria; performing measurement analysis of subjects with a specific diagnosis and medication; and calculating measure outcomes of subjects over time.

Discussion

MAV-clic supports clinicians and healthcare analysts by efficiently stratifying subjects to understand specific scenarios and optimize decision making.

Conclusion

MAV-clic is founded on the scientific premise that to improve the quality and transition of healthcare, integrative platforms are necessary to analyze heterogeneous clinical, epidemiological, metabolomics, proteomics, and genomics data for precision medicine.