Abstract
High quality clinical research data provide 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.
Introduction
Before discussing methods of assuring data quality, one must determine exactly what is meant by terms such as “quality,” “quality control” (QC) and “quality assurance” (QA). The American Society for Quality (ASQ) provides the following definitions for these terms.
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Although the ultimate goal of CDM personnel is to complete a study with a quality dataset, proper principles and practices must be employed throughout the course of a study to ultimately ensure quality. If a study’s design, protocol or case report forms (CRFs) are of insufficient quality, the study is unlikely to accurately provide answers to its hypotheses. Lack of quality processes in any part of a clinical study can lead to results that are distorted, missing or inaccurate.
Scope
This chapter emphasizes the infrastructures and practices that those managing clinical research data should use to ensure data quality. Although quality measurement methods are a necessary part of a plan to obtain quality data, a larger emphasis should be placed on error prevention, both in organizational infrastructure and early in the design stages of each protocol. For information about identifying and quantifying errors in clinical research data, see the Good Clinical Data Management Practices (GCDMP) chapter entitled “Measuring Data Quality.”
Many of the tasks described in this chapter may be joint responsibilities between different groups, just as many different groups may be involved in the implementation of various tasks. However, in all cases clinical data managers need to be conscious of whether or not these tasks have in fact been performed in a satisfactory manner.
Minimum Standards
Design and maintain data-handling processes according to the organization’s documented quality system.
Attempt to collect only data that are essential for interpretation of study results and that are required by the protocol.
Provide sufficient information in data-processing documentation to reproduce final analyses from source data.
Assure data quality for all studies, whether submitted for regulatory review or not (e.g., marketing studies, observational studies or for publication-only studies).
Ensure data quality is appropriate for study analyses according to parameters laid out in a statistical analysis plan, if one exists. Appropriate levels of data quality for analyses should always be determined by an experienced statistician.
Use company-standardized data collection and handling processes.
Best Practices
Have an organizational quality policy that is strongly supported by upper management, understood by all staff, and supported by operational procedures.
Create and maintain documentation of all roles and responsibilities involved in managing a clinical study.
Use industry-standardized data collection and handling processes.
Use well-documented processes for data collection and handling.
Minimize the number of data-processing steps in order to minimize potential sources of error.
Focus on error prevention with QA and focus on process monitoring with QC. The final product (database or software) of the clinical study should not be the focus of QA or QC.
Ensure data quality audits assess compliance of procedures to regulations, compliance of practices to written documentation, conformance of data to source documentation, and conformance of data to written procedures.
Apply data QC to each step of data management processes.
Ensure all data management personnel are trained on and knowledgeable of the organization’s quality policy.
Quality Systems
A quality system encompasses the organizational structure, responsibilities, procedures, processes, and resources that are necessary to implement quality management.9 This approach was standardized by the International Organization for Standardization (ISO) and is applicable across many industries, including clinical research. A quality system approach advocates an infrastructure that provides the flexibility to account for study differences in a controlled and consistent manner. Although not mandated for all clinical studies, a quality system approach has been adopted by the FDA in medical device regulations.10
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A quality system approach is most powerful when employed by an entire organization, covering the entire clinical research process. Although a single department can achieve high performance in isolation, only local optimization will be achieved, which may not fully align with organizational goals.
Quality System Documentation
Quality Policy
An organization’s quality policy is the highest level of a quality system. Specified by top management, the quality policy communicates and documents an organization’s overall intentions and direction with respect to quality. The quality policy should detail various levels of the organization’s quality system, such as management review procedures, the quality manual and the quality plan.8
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The ACDM has also published ACDM Guidelines to Facilitate Production of a Data Handling Protocol (DHP guidelines).12 These guidelines provide an outline and list of items to be covered in an organization’s study-specific procedures. Organizations may customize the content of the data-handling protocol, adjusting the level of detail to correspond to the level of detail present in their SOPs. The DHP guidelines are an excellent reference for defining and developing organizational study-specific procedures. Such references only provide a framework, however, and the content should be specific to the organization. For more information, see the GCDMP chapter entitled “Data Management Plan.”
Creating a Quality System
Structuring a CDM Quality System
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Opportunities for standardization may vary from organization to organization. For example, a large pharmaceutical company has more potential for standardization than does a contract research organization (CRO). For more information about standards used within clinical research, see the GCDMP chapter entitled “Data Management Standards in Clinical Research.”
Maintaining a Quality System
Once a quality system has been created, an organization’s leadership should encourage proactive maintenance of the quality system. Corporate policies often predefine the methods by which maintenance is performed. Whatever methodology is employed at a corporate level, it should not preclude employees from critiquing processes or proposing more effective and efficient practices.
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Because organizations always experience change, a quality system must be able to accommodate changes. Once a quality system has been created, it should also be reviewed on a regular basis. The review may use a predetermined corporate methodology or be an ad hoc review of quality system components. Either way, if changes need to be made to the original quality system components, these changes must be reviewed and approved. Once changes have been made, all relevant personnel should be retrained on new quality system components to ensure proper implementation of the quality system.
Auditing a Quality System
The word “auditing” is described by the ASQ as a systematic and independent examination to determine whether quality activities and related results comply with planned arrangements, and whether these arrangements are implemented effectively and are suitable to achieve objectives.3
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Written CDM procedures should be compliant with regulatory requirements and should specify process steps and decision points required for handling and processing clinical data, including instructions for manual reviews, data-entry conventions, and data clarification procedures. Written procedures should be specific enough to enable the clinical database to be reproduced using source documentation. To determine the level of compliance with regulatory requirements, an auditor compares CDM procedures with current regulations.
Documented compliance of the CDM organization or department to its written policy should exist, consisting of objective evidence that the written data-handling procedures were followed. This evidence can include a database audit trail, signed and dated checklists, signed data clarification forms from a site, or interviews with CDM personnel.
Objective evidence should exist to indicate that CDM processes result in quantifiably high-quality, reliable clinical data for analysis and regulatory review. Several steps are required to obtain objective evidence that CDM processes produce reliable clinical data for analysis and regulatory review. The first step is quantifying the quality of clinical data, which is usually represented by an error rate. Additional objective evidence may include data demonstrating that an organization’s data-handling process is operating in a state of control. Another important type of evidence is an assessment of the potential impact of the error rate on interpretations of data and conclusions that are ultimately derived from the data. This type of assessment may be carried out by departments outside of CDM, but the results provide CDM with information that may ultimately improve CDM processes.
Other Considerations for Quality Systems
Different types of studies require different considerations in relation to QA.
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For more information concerning data quality from external data sources, please see the GCDMP chapters entitled “Laboratory Data Handling,” “External Data Transfers” and “Vendor Selection and Management.”
Recommended Standard Operating Procedures
- Development and Maintenance of Standard Operating Procedures
- Development of Planned Deviations from Standard Operating Procedures
- Development and Maintenance of Study-specific Procedures
- Quality Assurance Audits