Data Privacy. Journal of the Society for Clinical Data Management. 2023; 1(1): 20, pp. 1–5. DOI: https://doi.org/10.47912/jscdm.341

Keywords: Clinical Data Management; Data Privacy; Good Clinical Practice

The privacy of any subject who participates in a clinical study must be protected for ethical and legal reasons. Clinical data management professionals must be familiar with privacy laws that exist for the regions in which clinical studies are occurring and ensure all reasonable and appropriate precautions are taken. This chapter discusses strategies and considerations that data managers must understand and follow, including the varying types of personal data in clinical studies, best practices for securing and protecting data (both paper and electronic), methods of data collection, and strategies for ensuring that personnel, both internal and external (e.g., vendors), follow applicable data privacy standards.

Project Management for the Clinical Data Manager. Journal of the Society for Clinical Data Management. 2023; 1(1): 19, pp. 1–7. DOI: https://doi.org/10.47912/jscdm.340 

Keywords: Clinical Data Management; Project Management

Clinical data managers often assume some degree of project management responsibilities. This chapter discusses the discipline of project management and how to effectively apply project management principles to clinical data management. The chapter describes specific project management activities within a clinical data management department, and discusses the desired competencies of a data manager assuming project management responsibilities.

Design and Development of Data Collection Instruments. Journal of the Society for Clinical Data Management. 2023; 1(1): 22, pp. 1–7. DOI: https://doi.org/10.47912/jscdm.339

Keywords: Clinical Data Management; Case Report Form; Good Clinical Practice

Clinical data can be collected with a variety of tools, but case report forms are the most frequently used data collection tool. Case report forms may be paper based or electronic and include data entry forms used by patients as well as health care providers. This chapter provides guidelines for the design of case report forms, emphasizing accurate, consistent and logical data collection in accordance with a study’s protocol. The design and development processes discussed highlight the importance of a case report form’s clarity and ease of use. The chapter also discusses referential questions, redundancies, edit checks, standards, case report form completion guidelines, and distinctions for studies using paper CRFs, electronic data capture and/or patient-reported outcomes.

Edit Check Design Principles. Journal of the Society for Clinical Data Management. 2023; 1(1): 18, pp. 1–8. DOI: https://doi.org/10.47912/jscdm.338

Keywords: Clinical Data Management; Global Library; Edit Checks

Edit checks are invaluable tools for increasing data quality and providing greater efficiency during data review and cleaning activities. This chapter discusses the process of edit check creation, including balance and efficiency considerations. The chapter also describes different types of edit checks, edit check validation, strategies for edit check specification creation, training related to edit checks, and considerations for using edit checks in studies that are paper based or use electronic data capture.

Safety Data Management and Reporting. Journal of the Society for Clinical Data Management. 2023; 1(1): 5, pp. 1–8. DOI: https://doi.org/10.47912/jscdm.325

Keywords: Clinical Data Management; Safety Data Management; Clinical Data Reconciliation; Good Clinical Practice

Collecting and reporting information about the safety of an experimental compound or product constitutes a significant challenge for clinical data management. This chapter reviews the wide range of factors that must be considered for the successful completion of a project’s safety data management and reporting responsibilities. Industry guidelines and regulations for collecting and reporting reliable, high-quality safety data are discussed. The importance of degrees of precision and descriptions of severity when capturing data about adverse events is emphasized. The use of medical dictionaries, especially MedDRA, is reviewed with consideration for the process of encoding safety data to dictionary terms and various approaches to this task. Laboratory data and other forms of data, such as specialized tests, are discussed as potential sources of safety data. Special consideration is given for the capture of serious adverse events and their reporting to regulatory agencies. General issues to consider when reporting safety data to the FDA are also discussed.

Serious Adverse Event Data Reconciliation. Journal of the Society for Clinical Data Management. 2023; 1(1): 4, pp. 1–3. DOI: https://doi.org/10.47912/jscdm.324

Keywords: Clinical Data Management; Serious Adverse Event data management; data reconciliation; Good Clinical Practice

Because serious adverse event (SAE) data are typically stored in a safety database separate from the clinical trial data, a reconciliation of the two datasets must be carried out to ensure consistency. In covering the procedures for completing this task, this chapter discusses the importance of cooperating with safety representatives, and of creating proper documentation of discrepancies, missing data, reconciliation and other issues encountered during this process.

Training. Journal of the Society for Clinical Data Management. 2023; 1(1): 2, pp. 1–5. DOI: https://doi.org/10.47912/jscdm.322

Keywords: Clinical Data Management; Good Clinical Practice; Training Records

Clinical data management employees must receive the training necessary to complete their project-related responsibilities effectively and successfully. This chapter reviews the various factors to consider when adopting a training program for CDM employees. Approaches to the development of master training plans and training plans for individual employees are discussed. Topics which should be covered in data management training are reviewed. Effective strategies for facilitating the learning process are presented, including an overview of the principles of learning and different techniques for introducing course material to trainees. Online training is introduced as a solution to time and logistical constraints, and considerations for choosing and developing online training are reviewed. Trainer qualifications, the training environment, and evaluation and feedback from trainees are included as important factors to consider when adopting and maintaining a training program.

Metrics in Clinical Data Management. Journal of the Society for Clinical Data Management. 2023; 1(1): 11, pp. 1–9. DOI: https://doi.org/10.47912/jscdm.331

Keywords: Clinical Data Management; Data Reporting Metrics; Data Analysis, Good Clinical Practice

A wide range of measurements, commonly referred to as “metrics,” are essential to evaluate the progress and outcomes of a clinical study. This chapter considers various metrics used in clinical data management, as well as the process of selecting metrics that are related to the goals and objectives of an organization. The chapter discusses the importance of standardizing metrics for a project and across projects, and gives suggestions to help ensure metrics are provided in a timely fashion with adequate contextual information to be understood and effectively used to measure, monitor performance and improve efficiencies.

Patient-Reported Outcomes. Journal of the Society for Clinical Data Management. 2023; 1(1): 13, pp. 1–5. DOI: https://doi.org/10.47912/jscdm.333

Keywords: Clinical Data Management; patient questionnaires; clinician questionnaires

Clinical studies frequently rely on patient-reported outcomes to fully evaluate the efficacy of a drug, device or treatment. This chapter differentiates between traditional and electronic methods of capturing patient-reported outcomes and discusses features of each approach. The chapter also examines the impact of regulatory requirements on patient-reported data collection.

Laboratory Data Handling. Journal of the Society for Clinical Data Management. 2023; 1(1): 15, pp. 1–8. DOI: https://doi.org/10.47912/jscdm.335

Keywords: Clinical Data Management; Local Lab, Central Lab; Lab Data Management; Good Clinical Practice

The vast majority of clinical studies use laboratory data, which should be treated with the same rigorous attention to detail and data quality as any other clinical data. This chapter describes different types of laboratories, different types of laboratory data, and important elements of laboratory data handling. In particular, the chapter discusses the importance of standards and reference ranges for laboratory data, as well as principles and processes to help ensure the accuracy and integrity of all laboratory data.

Medical Coding Dictionary Management & Maintenance. Journal of the Society for Clinical Data Management. 2023; 1(1): 6, pp. 1–6. DOI: https://doi.org/10.47912/jscdm.326

Keywords: Clinical Data Management; MedDRA; WHODrug; medical coding dictionaries; Good Clinical Practice

The use of medical coding dictionaries for medical terms data such as adverse events, medical history, medications, and treatments/procedures are valuable from the standpoint of minimizing variability in the way data are reported and analyzed. This chapter discusses the importance of medical coding dictionaries in streamlining and improving the quality of medical terms data obtained during collection and coding. Furthermore, reconciliation of medical terms data between a safety database and a clinical database is improved with the use of medical coding dictionaries during a clinical study. Issues that can affect conversion of reported terms to dictionary terms are considered, including autoencoders, the use of coded terms, and dictionary and software change control and versioning. Due to their widespread use, MedDRA and WHO Drug are discussed in more detail than other dictionaries.

Measuring Data Quality. Journal of the Society for Clinical Data Management. 2023; 1(1): 9, pp. 1–5. DOI: https://doi.org/10.47912/jscdm.329

Keywords: Clinical Data Management; Clinical Data Quality; Good Clinical Practice

Data collected during a clinical trial must have as few errors as possible to be able to support the findings or conclusions drawn from that trial. Moreover, proof of data quality is essential for meeting regulatory requirements. This chapter considers the challenges faced by clinical data management professionals in determining a dataset’s level of quality, with an emphasis on the importance of calculating error rates. An algorithm for calculating error rates is presented in this chapter and is asserted to be the preferable method for determining the quality of data from a clinical trial.

Database Validation, Programming and Standards. Journal of the Society for Clinical Data Management. 2023; 1(1): 16, pp. 1–7. DOI: https://doi.org/10.47912/jscdm.336

Keywords: Clinical Data Management; Software development lifecycle; Good Clinical Practice

Success of any clinical study depends on the quality and integrity of its final database. Validation of the software system and database used for a study are crucial risk-focused quality processes for assuring and ensuring quality and integrity. This chapter discusses principles and types of validation, as well as common validation risks. Although system validation is discussed, the primary focus of the chapter is on study-specific validation, which has a greater direct impact on clinical data managers.

Database Closure. Journal of the Society for Clinical Data Management. 2023; 1(1): 1, pp. 1–6. DOI: https://doi.org/10.47912/jscdm.321

Keywords: Clinical Data Management; Clinical Database Closure; Good Clinical Practice

Study databases must have access removed and be properly closed to ensure data integrity for the generation of results, analyses, and submissions. This chapter recommends processes, checklists, and essential documentation for locking and closing study databases. Reopening a locked data- base to evaluate and correct errors is discussed, with an examination of important considerations and decisions that should be made, procedures that should be followed, and documentation that must be produced.

Data Storage. Journal of the Society for Clinical Data Management. 2023; 1(1): 8, pp. 1–3. DOI: https://doi.org/10.47912/jscdm.328

Keywords: Clinical Data Management; Clinical Data Storage; Good Clinical Practice

The storage of data that is collected during a clinical trial must be carefully planned. This chapter discusses issues that should be considered whether a study’s data is stored electronically or on paper. Guidelines for securely storing data are provided, with an emphasis on preventing unauthorized access that could detract from the integrity of a study. Issues concerning passwords, access controls, electronic signatures (including 21 CFR 11), and audit trails are considered. Recommendations for the locking and archival of data at the conclusion of a study are detailed.

External Data Transfers. Journal of the Society for Clinical Data Management. 2023; 1(1): 14, pp. 1–5. DOI: https://doi.org/10.47912/jscdm.334

Keywords: Clinical Data Management; Data Transfer Specifications; Test Data Transfer; Non-EDC Data

Data collected from external sources can be essential to the quality of a clinical trial. This chapter reviews some of the types of external data that may be utilized within a clinical trial and discusses the best practices for handling such data. Processing steps for the validation, editing, and verification of external data are examined, and the importance of key variables is emphasized. Discussions are included concerning file and record formats, transmission of data, procedures for database updates, and archiving of external data.

Data Entry Processes. Journal of the Society for Clinical Data Management. 2023; 1(1): 7, pp. 1–8. DOI: https://doi.org/10.47912/jscdm.327

Keywords: Clinical Data Management; Data Collection; Data Handling; Good Clinical Practice

Established procedures for data receipt and entry are necessary for a study to successfully produce a clinical database of sufficient quality to support or refute study hypotheses. This chapter discusses considerations needed to reduce the likelihood of errors occurring during data entry processes and ensure consistency in a clinical database. These considerations cover topics including workflow components, data receipt and tracking, data entry, data review, data cleaning, and change control for case report forms, databases, and processes.

CDM Presentation at Investigator Meetings. Journal of the Society for Clinical Data Management. 2023; 1(1): 12, pp. 1–3. DOI: https://doi.org/10.47912/jscdm.332

Keywords: Clinical Data Management; Investigator Meetings; Good Clinical Practice

Clinical data management professionals serve an important role at investigator meetings, especially when the trial is large, complex or multisite. This chapter covers the procedures clinical data management professionals should follow when preparing a presentation for such meetings, including presenting examples of case report forms, discussing various types of error- checks, reviewing the role of the data manager, and emphasizing the proper use of data clarification forms.

Clinical Data Archiving. Journal of the Society for Clinical Data Management. 2023; 1(1): 3, pp. 1–4. DOI: https://doi.org/10.47912/jscdm.323

Keywords: Clinical Data Management; Clinical Data Archiving; Good Clinical Practice

In order to meet the requirements of industry guidelines and regulations, clinical data managers must ensure that data captured during a clinical trial are retained correctly. This chapter provides an overview of the regulations that must be followed and discusses approaches to satisfying the requirements. Consideration is given to proper handling of electronic data that are collected in a clinical trial. The components that constitute a clinical data archive are reviewed, and technical requirements for the correct use of open electronic data formats, such as XML (Extensible Markup Language) and SAS®, are discussed with an emphasis on ensuring long-term accessibility.

Assuring Data Quality. Journal of the Society for Clinical Data Management. 2023; 1(1): 10, pp. 1–8. DOI: https://doi.org/10.47912/jscdm.330

Keywords: Clinical Data Management; Clinical Data Integrity; Clinical Quality Assurance

High quality clinical research data provides the basis for conclusions regarding the safety and efficacy of a medical treatment. This chapter discusses how the terminology and methodology for assuring quality, already well established in other industries, can be applied successfully to clinical research. General principles of quality systems and quality assurance in clinical data management are discussed. The key differences between quality assurance and quality control are presented and the roles of standardization, standard operating procedures, and auditing are reviewed.

CRF Printing and Vendor Selection. Journal of the Society for Clinical Data Management. 2023; 1(1): 17, pp. 1–3. DOI: https://doi.org/10.47912/jscdm.337

Keywords: Clinical Data Management; Vendor Selection; Case Report Form Printing; Good Clinical Practice

Planning for the printing of a study’s case report forms (CRFs) is essential to the study’s conduct. This chapter provides insight and guidance for this critical component. Guidelines for the evaluation and selection of CRF printing vendors are provided. The chapter also covers the process by which a clinical data manager plans for the production of printed CRFs and their timely delivery to sites, with both tasks completed by a third-party vendor. Guidelines for the CRF binder, the paper used for printing, and tabs banks are discussed in regard to the specifications that should be provided to the printing vendor. Recommendations are made for binding, packaging, and shipping the CRFs, with an emphasis on the importance of timetables. Guidelines for the evaluation and selection of CRF printing vendors are provided. An example of a CRF printing specifications checklist is included.