Abstract
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.
Introduction
Safety data often present the most challenging aspects of the management and reporting of clinical trial data. Consideration for return-on-investment frequently curtails the query process for cleaning safety data and limits reporting methods.
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The processes of data capture, management, and reporting are highly integrated. Considerations of best practices for reporting guidelines would be deficient in absence of guidelines for the earlier processes.
Scope
To the clinical trial scientist, the safety data in a clinical study are simultaneously a rich source of information and an enormous challenge. The data manager and statistician who are a part of the product team must work closely with each other and with other team members to ensure that safety data are captured in a sensible way to facilitate proper interpretation and meaningful analysis and summary. Ensuring quality requires that the team capture, process, and report the data in a way that facilitates the drawing of reliable conclusions. When determining the balance between business and science, data managers and statisticians must consider that resources may be expended on efforts that have no effect on conclusions.
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This chapter discusses practices, procedures, and recommendations for data managers to operate within the project team and to work closely with statisticians, monitors, and clinical research so that data management practices support statistical and medical purposes. Data managers are better equipped to function as fully-integrated team members when they have a basic understanding of the activities and needs of other team members, particularly statisticians.
Minimum Standards
When considering the capture, management, analysis, and reporting of safety data, the following minimum standards are recommended:
Ensure compliance with regulations.
Ensure that the standard of quality supports the utilization of the data.
Ensure that conclusions about the safety profile of a compound can be reliably drawn from the database.
Ensure that safety risks are identified and reported accurately.
Ensure that normal ranges are properly linked to laboratory data. If normal ranges are unavailable, ensure that the reference ranges which are used are documented as such. This standard is especially crucial when normal ranges are updated frequently.
Best Practices
When considering the capture, management, analysis, and reporting of safety data, the following best practices are recommended:
Develop CRFs with teams of individuals from the monitoring, data management, statistics, regulatory affairs, and medical departments, thereby ensuring adequate attention to the collection of safety data.
Consider the level of precision that can be attained in the study and select the CRF format for collecting AEs appropriate for that level. Also, consider the level of precision in the analysis.
Define severity, with an understanding of its uses and limitations.
Examine laboratory data from the perspectives of categorical shifts, changes in magnitude for the group, individual significant values or changes, and listings. Consider related parameters for compounds with potential toxicity in specific body systems.
Consider laboratory normalization techniques when combining data across studies or centers where varying normal ranges are used.
Include data managers and statisticians working together when considering computerization, management, reporting, and analysis of safety data. These tasks are highly integrated and require joint considerations of individual team constituents. Develop standard operating procedures (SOPs) for data capture, data validation, statistical analysis, and reporting of data. The SOPs should include guidelines for this team approach.
Document the status and quality of safety data, and include this documentation with the database.
Include clear links for comparators, such as normal ranges for laboratory data, with the database.
Consider levels of precision in the capture and the reporting of safety data to reduce the likelihood of over-interpretation or misinterpretation.
Understand that time-to-event analyses are only meaningful when the timing of the event is reliably known.
Consider both categorical shifts (from a status of normal to abnormal) and magnitude changes for analysis and reporting of laboratory data. An examination of significant values may provide different information from an examination of significant changes
Apply standards commensurate with the utilization of the results residing in the databases when using databases for safety reporting (e.g., expedited reporting, ongoing review by monitoring boards, or routine reporting). If important decisions will be made based on the information in the database, know the data’s appropriateness and level of quality.
Available Guidelines
One definition of “quality data” is “a collection of data from which reliable conclusions can be drawn.” The goal of reporting safety data is to convey information that would facilitate the drawing of reliable conclusions. Generally, one of the key objectives in investigative clinical research trials, is to characterize, investigate, establish, or confirm the safety profile of an investigational product. The management and reporting of the safety data from the trial should support that objective.
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Other guidance documents that give advice for capturing, managing, and reporting safety data are available from the ICH and from regulatory agencies. Sponsors should refer to IND regulations (21 CFR 312) and NDA regulations (21 CFR 314) to ensure compliance with FDA regulations for investigational and marketed products.
Safety Reporting
Safety data are reported and examined at various stages of an investigation and by different assessors. IND regulations specify expedited reporting for serious or alarming adverse events. Many studies have safety data monitoring boards (SDMB) that review data as they accumulate in a study. The sponsor’s medical monitor reviews safety data, frequently masked to the treatment. Then, after market approval, there are NDA regulations that specify safety reporting. Data managers and statisticians need to ensure that the reports provided are supported by the quality appropriate for the purpose of the report.
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Any review of safety data that is based on reported information from a safety database (as opposed to CRFs) relies on that database. If the quality is poor, the decisions taken may be wrong. Review of accumulating data often implies a mixture of complete data with partial data and a mixture of clean data with dirty data. To provide the optimal information to the users of the dynamic database, the quality should be known and reported to the reviewers with the safety data. However, it is generally not helpful to report to data reviewers that some data are dirty without specifically identifying which data are dirty.
Capture, Management, And Reporting Of Adverse Events
Clinical adverse events frequently house the most important safety information in a clinical study. Ensuring that methods of collection, coding, analysis, and reporting facilitate the drawing of reliable conclusions requires an understanding of the characteristics and limitations of adverse event data.
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A common data display that is encouraged by the ICH and the FDA is a breakdown by severity. In this context, it is easy to confuse severity with seriousness or to misinterpret severity altogether. A breakdown that ignores the particular events and that counts mild AEs separately from moderate AEs will give a distorted assessment when the same study includes reports of “mild stroke” or “mild MI” and also reports of “severe rash” or “severe sleepiness.” A more meaningful display breaks down severity within a particular event.
Dictionaries
AE dictionaries are needed to group data for meaningful analysis. MedDRA is the ICH-developed and recommended dictionary for all medical events captured in clinical trials, including, but not limited to, AEs.
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To ensure reproducibility, the version of the dictionary used in any study should be stored with the database.
Encoding
Auto-encoding is a highly recommended practice to facilitate the execution of a dictionary against AEs. Auto-encoding software is available to assist with the programming aspect of this task. To cultivate an understanding of the coding process, training of the monitors and site personnel should facilitate capture of AE data in a format that can be auto-encoded. Training should include guidelines such as the following:
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Coding of AEs has significant impact on the analysis and interpretation of the safety data for a product. The perspective that coding is a clerical function is naïve and risky. As the world moves toward the full implementation of MedDRA, the role of coding will have an even greater impact on the interpretation of safety data.
Capture, Management, and Reporting of Laboratory Data
The characteristics of laboratory data differ importantly from most other types of data. Most clinical adverse events can be observed by either the subject or the physician. However, an elevation in bilirubin or cholesterol is not generally observable. For example, even in high-precision studies, it is impossible to know the time of an elevation of a clinical chemistry analyte. At the time of a blood draw, whether or not the value is elevated can be known, but when the value became elevated is unknown.
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Summaries by related groups of analytes are useful for some studies or integrated summaries. For example, products that may be prone to cause liver damage may need careful examination of analytes that relate to hepatic function. For the hepatic-function-related analytes, it may be useful to prepare a summary on a single page that includes proportions of subjects who double the baseline, triple the baseline, have a change of fixed magnitude, or exceed an alert or toxic threshold.
Other Data
Safety data can have forms other than AEs and laboratory values. Capture of data from specialty tests (e.g., electrocardiograms, electroencephalographs) requires an understanding of the common data derived from the test and of the format, precision, and special attributes of the data.
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One method used by some sponsors that avoids computerization of verbose commentary is codification, in which a medically qualified individual reads the information and judges it to be relevant, not relevant, or perhaps critical. A code can be applied and keyed, where “0=no comment,” “1=comment, not relevant,” “3=comment, relevant,” and “4=comment, critical.”
Serious Adverse Event Data
Expedited reports are required by regulatory agencies for certain serious adverse events. In many companies, receiving reports of serious adverse events (SAEs), computerizing these reports, and managing these reports is the responsibility of a dedicated group of individuals. Often, this group is separate from the data management group that is responsible for computerizing and managing data reported from clinical trials.
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These two types of databases generally have important differences in their sources, their quality levels, their uses, and their customers. Reconciliation of SAE data and the clinical trial database that houses the relevant SAE reports is not always straightforward. Different sponsors have vastly different methods of managing these two databases.
Safety Data Management and Reporting - Page 17 of 22 - Good clinical data management practices include provisions for reconciling important disparities between serious adverse events that are captured both in the SAE database and in the clinical trial database. The business-balance perspective encourages users of these databases to recognize that clinical trial databases may be queried or updated while SAE databases are not and that, consequently, some discrepancies may exist because preliminary medical judgments were later changed in light of updated information.
General Safety Data
The FDA draft document Reviewer Guidance: Conducting a Clinical Safety Review of a New Product Application and Preparing a Report on the Review (November 1996) provides specific guidance to industry that reflects thinking within the FDA about safety data.
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When the study employs one investigator who may be on the staff of several hospitals, or when a cluster of hospitals shares equipment and has common SOPs, or when a study makes heavy use of referrals, these attributes affect the interpretation of the center’s effects. Reporting data in a multi-center study requires understanding the source of variability among centers and the reasonableness of displaying data by center or by clusters of centers.
Recommended Standard Operating Procedures
Coding of Adverse Events
Maintenance of Coding Dictionaries
Reconciliation of Serious AEs in SAE Database with Clinical Trial Database
Management of AE Analysis File
Management of Laboratory Data and Normal Ranges
Preparing Integrated Summaries of Safety Data