The development and analysis of a Japanese modern comprehensive clinical data management training program.

Background: Clinical data management (CDM) collects, integrates, and makes data available. It plays a vital role in clinical research. However, there are few opportunities for Japanese clinical data managers to learn about its systematic framework, particularly in academic research organizations. While Japanese-language CDM training exists, its effectiveness in a Japanese context requires clarification. Objectives: We aimed to develop an advanced program of instruction for professionals to understand CDM and to determine the effectiveness of the training program. Methods and results: We developed an advanced program including risk-based monitoring and the Clinical Data Interchange Standards Consortium on a trial basis for clinical data managers to provide them with a comprehensive understanding of CDM. Fifty-two people attended the program and reported that they were highly satisfied with it. Conclusions: To provide comprehensive CDM training in Japan, it is imperative to continue improving the content and develop an advanced program. Due to the recent tightening of clinical research regulations and the development and dissemination of various systems for conducting clinical research, the competency-based educational program requires further development.

A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study.

Background: In recent years, many researchers have focused on the use of legacy data, such as pooled analyses that collect and reanalyze data from multiple studies. However, the methodology for the integration of preexisting databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM. Objective: This study aimed to design a practical model for integrating preexisting databases using the CDISC SDTM. Methods: Data integration was performed in three phases: (1) the confirmation of the variables, (2) SDTM mapping, and (3) the generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the data sets were converted to a vertical structure. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the Research Electronic Data Capture (REDCap) field annotation. In phase 3, the data dictionary, including the SDTM metadata, was outputted in the Operational Data Model (ODM) format. Finally, the mapped SDTM data were generated using REDCap2SDTM version 2. Results: SDTM data were generated as a comma-separated values file for each of the 7 domains defined in the metadata. A total of 17 items were commonly mapped to 3 databases. Because the SDTM data were set in each database correctly, we were able to integrate 3 independently preexisting databases into 1 database in the CDISC SDTM format. Conclusions: Our project suggests that the CDISC SDTM is useful for integrating multiple preexisting databases.

Best Practice Recommendations for Electronic Patient-Reported Outcome Dataset Structure and Standardization to Support Drug Development.

The electronic patient-reported outcome (ePRO) Dataset Structure and Standardization Project is a multistakeholder initiative formed by Critical Path Institute's PRO Consortium and Electronic Clinical Outcome Assessment (eCOA) Consortium to address issues related to ePRO dataset structure and standardization and to provide best practice recommendations for clinical trial sponsors and eCOA providers. Given the many benefits of utilizing electronic modes to capture PRO data, clinical trials are increasingly using these methods, yet there are challenges to using data generated by eCOA systems. Clinical Data Interchange Standards Consortium (CDISC) standards are used in clinical trials to ensure consistency in data collection, tabulation, and analysis and to facilitate regulatory submission. Currently, ePRO data are not required to follow a standard model, and the data models used often vary by eCOA provider and sponsor. This lack of consistency creates risks for programming and analysis and difficulties for analytics functions generating the required analysis and submission datasets. There is a disconnect between data standards used for study data submission and those used for data collection via case report forms and ePRO forms, which would be mitigated through the application of CDISC standards for ePRO data capture and transfer. The project was formed to collate and examine the issues arising from the lack of adoption of standardized approaches and this paper details recommendations to address those issues. Recommendations to address issues with ePRO dataset structure and standardization include adopting CDISC standards in the ePRO data platform, timely involvement of key stakeholders, ensuring ePRO controls are implemented, addressing issues of missing data early in development, ensuring quality control and validation of ePRO datasets, and use of read-only datasets.

Clinical Data Interchange Standards in Clinical Trials on Alzheimer's Disease.

OBJECTIVE: The Clinical Data Interchange Standards Consortium (CDISC) proposed outcome measures for clinical trials on Alzheimer's disease (AD) in the Therapeutic Area User Guide for AD (TAUG-AD). To investigate how well the clinical trials on AD registered in the ClinicalTrials.gov complied with the recommendations on outcome measures by the CDISC. METHODS: We compared the outcome measures proposed in the TAUG-AD version 2.0.1 with those employed in the protocols of clinical trials on AD registered in ClinicalTrials.gov. RESULTS: We analyzed 101 outcome measures from 305 protocols. The TAUG-AD listed ten scales for outcome measures of clinical trials on AD. The scales for cognition, activities of daily living, behavioral and psychological symptoms of dementia, and global severity listed in TAUG-AD were most frequently employed in the clinical trials on AD. However, TAUG-AD did not include any scale on quality of life. Also, several scales such as Montreal Cognitive Assessment, Alzheimer's Disease Cooperative Study-Activities of Daily Living, and Cohen- Mansfield Agitation Inventory not listed in the TAUG-AD were commonly employed in the clinical trials on AD and changed over time. CONCLUSION: To properly standardize the data from clinical trials on AD, the gap between the TAUG-AD and the measures employed in real-world clinical trials should be filled.

Harmonisation of large-scale, heterogeneous individual participant adverse event data from randomised trials of statin therapy.

BACKGROUND: Meta-analyses of individual-level data from randomised trials are often required to detect clinically worthwhile effects. The Cholesterol Treatment Trialists' Collaboration, which includes data from numerous large long-term statin trials, is conducting a review of the effects of statin therapy on all adverse events collected in those trials. This article describes the approaches used and challenges faced to systematically capture and categorise the data. METHODS: Protocols, statistical analysis plans, case report forms, clinical study reports and datasets were obtained, reviewed and checked. Relevant baseline and follow-up data from each trial was then reorganised into standardised formats based upon the Clinical Data Interchange Standards Consortium Study Data Tabulation Model. Adverse event data were organised and coded (automatically or, where necessary, manually) according to a common medical dictionary based upon the Medical Dictionary for Regulatory Activities. RESULTS: Data from 23 double-blind statin trials and 5 open-label statin trials were provided, either through direct data transfer or through online access platforms. Together, these trials provided 845 datasets containing over 38 million records relating to 30,495 study variables and 181,973 randomised participants. Of the 46 Clinical Data Interchange Standards Consortium Study Data Tabulation Model domains that could potentially have been used to organise the data, the 13 most relevant to the project were identified and utilised, including 6 domains related to post-randomisation adverse events. Nearly 1.2 million adverse events were extracted and mapped to over 45,000 unique adverse event terms. Of these adverse events, 99% were coded to a Medical Dictionary for Regulatory Activities 'lower level term', with the remainder coded to a 'higher level term' or, very rarely, only a 'higher level group term'. CONCLUSION: In this meta-analysis of adverse event data from the large randomised trials of statins, approaches based on common standards for data organisation and classification have provided a resource capable of allowing reliable and rapid evaluation of any previously unknown benefits or hazards of statin therapy.

Application of ADaM in safety analysis of bioequivalence trail of generic drugs / 中国临床药理学与治疗学

AIM: To promote the Clinical Data Inter-change Standards Consortium (CDISC) standard in clinical trials and promote the standardization of clinical trial data. METHODS: To combine the implementation guide of Analysis Data Model (ADaM) and common problems of actual data, and to introduce the application of analytical data model ADaM in the safety of bioequivalence trails of generic drugs. RESULTS: For different types of clinical trial data, according to various situations that may occur, a safety analysis data set that meets the standards was generated. CONCLUSION: Under the background of the continuous development of generic drugs in China and the low degree of standardization of clinical trial data, the use of CDISC standards in clinical research can promote the standardization of clinical trial data, and can also shorten the time of statistical analysis and accelerate the process of drug development.

Application of Clinical Data Interchange Standards Consortium (CDISC) Standards to Electronic Data Capture System / 世界科学技术-中医药现代化

Data acquisition is one of the key links that influencing the quality of clinical research.Electronic data capture system (EDC) embodies the advantages of saving time,manpower and material resources and improving efficiency and reliability by data acquisition.CDISC has established worldwide industry standards to support the electronic acquisition,exchange,submission and archiving of clinical research data.The application of CDISC standards to EDC system is favorable in ensuring the validity and standardization of clinical data.This paper takes the Oracle's OC/ RDC (Oracle Clinical / Oracle Remote Data Capture) system as an example to discuss the application of CDISC standard to EDC system from the two aspects:direct application and indirect application.We suggest that data collection should be taken into account during the design phase of a clinical trial,and the CDISC standard be applied at the CRF design stage.A design for eCRF takes time and effort by the combination of EDC system and CDISC standard,while thoughtless design may collect the wrong data.Therefore,it is suggested that a specialized personnel should be put in charge of eCRF design and maintenance during the operation of EDC system,and a set of standardized eCRFs based on CDISC standard and standard operating procedures should be built in one organization.

Exploration on Internal and External Network Security Construction of Hospitals in the New Era / 医学信息学杂志

On the basis of summarizing the general situation of current internal and external network construction of hospitals,the paper introduces the main hidden dangers of hospital network security,puts forward internal and external network security management measures,and elaborates the internal and external network security construction principle,network topology structure,and network construction achievements of the First Affiliated Hospital of Guangxi Medical University.

Framework of sensor-based monitoring for pervasive patient care.

Sensor-based health systems can often become difficult to use, extend and sustain. The authors propose a framework for designing sensor-based health monitoring systems aiming to provide extensible and usable monitoring services in the scope of pervasive patient care. The authors' approach relies on a distributed system for monitoring the patient health status anytime-anywhere and detecting potential health complications, for which healthcare professionals and patients are notified accordingly. Portable or wearable sensing devices measure the patient's physiological parameters, a smart mobile device collects and analyses the sensor data, a Medical Center system receives notifications on the detected health condition, and a Health Professional Platform is used by formal caregivers in order to review the patient condition and configure monitoring schemas. A Service-oriented architecture is utilised to provide extensible functional components and interoperable interactions among the diversified system components. The framework was applied within the REMOTE ambient-assisted living project in which a prototype system was developed, utilising Bluetooth to communicate with the sensors and Web services for data exchange. A scenario of using the REMOTE system and preliminary usability results show the applicability, usefulness and virtue of our approach.

Pooled solifenacin overactive bladder trial data: Creation, validation and analysis of an integrated database.

BACKGROUND: Patient-level data are available for 11 randomized, controlled, Phase III/Phase IV solifenacin clinical trials. METHODS: Meta-analyses were conducted to interrogate the data, to broaden knowledge about solifenacin and overactive bladder (OAB) in general. Before integrating data, datasets from individual studies were mapped to a single format using methodology developed by the Clinical Data Interchange Standards Consortium (CDISC). Initially, the data structure was harmonized, to ensure identical categorization, using the CDISC Study Data Tabulation Model (SDTM). To allow for patient level meta-analysis, data were integrated and mapped to analysis datasets. Mapping included adding derived and categorical variables and followed standards described as the Analysis Data Model (ADaM). Mapping to both SDTM and ADaM was performed twice by two independent programming teams, results compared, and inconsistencies corrected in the final output. ADaM analysis sets included assignments of patients to the Safety Analysis Set and the Full Analysis Set. RESULTS: There were three analysis groupings: Analysis group 1 (placebo-controlled, monotherapy, fixed-dose studies, n = 3011); Analysis group 2 (placebo-controlled, monotherapy, pooled, fixed- and flexible-dose, n = 5379); Analysis group 3 (all solifenacin monotherapy-treated patients, n = 6539). Treatment groups were: solifenacin 5 mg fixed dose, solifenacin 5/10 mg flexible dose, solifenacin 10 mg fixed dose and overall solifenacin. Patient were similar enough for data pooling to be acceptable. CONCLUSIONS: Creating ADaM datasets provided significant information about individual studies and the derivation decisions made in each study; validated ADaM datasets now exist for medical history, efficacy and AEs. Results from these meta-analyses were similar over time.

A Preliminary Discussion of Application of CDISC Standards in Clinical Research Data Management of New Traditional Chinese Medicine / 中国中医药信息杂志

In this article, a new TCM clinical trial of phaseⅢ was served as an example of application of Clinical Data Interchange Standards Consortium (CDISC). It briefly introduced seven data acquisition modules commonly used in clinical research of new traditional Chinese medicine, namely demographics, subject characteristic, clinical event, medical history, questionnaire, laboratory inspection and adverse event. It also introduced the process of transferring the above modules to Study Data Tabulation Models (STDM), and discussed the feasibility and some issues that required attention of CDISC application in clinical research of new traditional Chinese medicine.

Clinical Data Acquisition Standards Harmonization importance and benefits in clinical data management.

In the clinical trial process, precise and concise data collection at the source is imperative and requires statistical analysis to be performed to derive the primary and secondary endpoints. The quality of raw data collection has a direct impact on the statistical outputs generated as per the statistical analysis plan. Hence, the data collection tools used for data transcription must be clear, understandable, and precise, which helps the investigator to provide the accurate subject data. Clinical Data Acquisition Standards Harmonization (CDASH) provides guidance to develop the case report form (CRF) for domains that are commonly used for the majority of the clinical trials across the therapeutic areas. This white paper describes the importance of CDASH standards, its advantages and its impact on the efforts and the cost in designing the CRF.

Therapeutic Area Data Standards for Autosomal Dominant Polycystic Kidney Disease: A Report From the Polycystic Kidney Disease Outcomes Consortium (PKDOC).

Data standards provide a structure for consistent understanding and exchange of data and enable the integration of data across studies for integrated analysis. There is no data standard applicable to kidney disease. We describe the process for development of the first-ever Clinical Data Interchange Standards Consortium (CDISC) data standard for autosomal dominant polycystic kidney disease (ADPKD) by the Polycystic Kidney Disease Outcomes Consortium (PKDOC). Definition of common data elements and creation of ADPKD-specific data standards from case report forms used in long-term ADPKD registries, an observational cohort (Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease [CRISP] 1 and 2), and a randomized clinical trial (Halt Progression of Polycystic Kidney Disease [HALT-PKD]) are described in detail. This data standard underwent extensive review, including a global public comment period, and is now available online as the first PKD-specific data standard (www.cdisc.org/therapeutic). Submission of clinical trial data that use standard data structures and terminology will be required for new electronic submissions to the US Food and Drug Administration for all disease areas by the end of 2016. This data standard will allow for the mapping and pooling of available data into a common data set in addition to providing a foundation for future studies, data sharing, and long-term registries in ADPKD. This data set will also be used to support the regulatory qualification of total kidney volume as a prognostic biomarker for use in clinical trials. The availability of consensus data standards for ADPKD has the potential to facilitate clinical trial initiation and increase sharing and aggregation of data across observational studies and among completed clinical trials, thereby improving our understanding of disease progression and treatment.

Value of shared preclinical safety studies - The eTOX database.

A first analysis of a database of shared preclinical safety data for 1214 small molecule drugs and drug candidates extracted from 3970 reports donated by thirteen pharmaceutical companies for the eTOX project (www.etoxproject.eu) is presented. Species, duration of exposure and administration route data were analysed to assess if large enough subsets of homogenous data are available for building in silico predictive models. Prevalence of treatment related effects for the different types of findings recorded were analysed. The eTOX ontology was used to determine the most common treatment-related clinical chemistry and histopathology findings reported in the database. The data were then mined to evaluate sensitivity of established in vivo biomarkers for liver toxicity risk assessment. The value of the database to inform other drug development projects during early drug development is illustrated by a case study.

Harmonization of detailed clinical models with clinical study data standards.

INTRODUCTION: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". BACKGROUND: Data sharing and integration between the clinical research data management system and the electronic health record system remains a challenging issue. To approach the issue, there is emerging interest in utilizing the Detailed Clinical Model (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator awarded Strategic Health IT Advanced Research Projects for normalization (SHARPn) project for normalizing patient data from the electronic health records (EHR). OBJECTIVE: The objective of the present study is to describe our preliminary efforts toward harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards. METHODS: We were focused on three generic domains: demographics, lab tests, and medications. We performed a panel review on each data element extracted from the CDISC templates and SHARPn CEMs. RESULTS: We have identified a set of data elements that are common to the context of both clinical study and broad secondary use of EHR data and discussed outstanding harmonization issues. CONCLUSIONS: We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoperability between systems for both clinical study and broad secondary use domains.

Minimum information about a biofilm experiment (MIABiE): standards for reporting experiments and data on sessile microbial communities living at interfaces.

The minimum information about a biofilm experiment (MIABiE) initiative has arisen from the need to find an adequate and scientifically sound way to control the quality of the documentation accompanying the public deposition of biofilm-related data, particularly those obtained using high-throughput devices and techniques. Thereby, the MIABiE consortium has initiated the identification and organization of a set of modules containing the minimum information that needs to be reported to guarantee the interpretability and independent verification of experimental results and their integration with knowledge coming from other fields. MIABiE does not intend to propose specific standards on how biofilms experiments should be performed, because it is acknowledged that specific research questions require specific conditions which may deviate from any standardization. Instead, MIABiE presents guidelines about the data to be recorded and published in order for the procedure and results to be easily and unequivocally interpreted and reproduced. Overall, MIABiE opens up the discussion about a number of particular areas of interest and attempts to achieve a broad consensus about which biofilm data and metadata should be reported in scientific journals in a systematic, rigorous and understandable manner.

Controlled vocabularies and ontologies in proteomics: overview, principles and practice.

This paper focuses on the use of controlled vocabularies (CVs) and ontologies especially in the area of proteomics, primarily related to the work of the Proteomics Standards Initiative (PSI). It describes the relevant proteomics standard formats and the ontologies used within them. Software and tools for working with these ontology files are also discussed. The article also examines the "mapping files" used to ensure correct controlled vocabulary terms that are placed within PSI standards and the fulfillment of the MIAPE (Minimum Information about a Proteomics Experiment) requirements. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

<b>Expectations and Challenges Regarding the Use of the SS-MIX in a Clinical Trial </b> / 薬剤疫学

While there is an advantage to be able to directly utilize some research database of medical information must solve several problems. It also includes support for international standardization, led by computerized system validation, and CDISC. We should countermeasures with epidemiological studies using SS-MIX standardized storage, in anticipation of its application to clinical trials in the near future in Japan. (Jpn J Pharmacoepidemiol 2013；18(1)：35-39)