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A system of checks and examinations that helps ensure the quality of clinical trials.
Pharmaceutical companies recognize the benefits of carefully managing the quality of data from their drug development and clinical trials. To ensure clinical data accuracy and integrity, it is necessary to thoroughly review these data, to assess the validity of outlying data points, and to carefully document query identification and resolution throughout a study's duration.
Maintaining accuracy and quality throughout a clinical study is a continual, dynamic process. Although study requirements are carefully set forth initially in detailed documents such as an approved clinical protocol, a data management plan, and an accompanying project plan, expectations and requirements can change during a study. This ongoing process requires revising mechanisms and communicating these revisions clearly to all investigators and support staff.
PHOTOGRAPHY: PHOTODISC ART DIRECTION: PAUL A. BELCI
Quality: the total set of characteristics of a product or service that affect its ability to satisfy a customer's stated or implied needs.
Quality system: the organizational structure, responsibilities, procedures, processes, and resources for implementing quality management.
Quality assurance (QA): the systematic and independent examination of all trial-related activities and documents. These audits determine whether the evaluated activities were appropriately conducted and that the data were generated, recorded, analyzed, and accurately reported according to protocol, standard operating procedures (SOPs), and good clinical practices (GCPs).4
Quality control (QC): periodic operational checks within each functional department to verify that clinical data are generated, collected, handled, analyzed, and reported according to protocol, SOPs, and GCPs.4
The ongoing challenge in managing the quality of clinical data is to continually monitor data collection procedures and data management practices at every level of the study. This includes:
Quality surveillance continues after the trial has ended and plays an important role in ensuring that:
The quality plan describes how the quality control and quality assurance processes will be applied throughout the clinical trial. It definitively defines the various quality-related tasks in the study. A quality plan documents specific quality practices, resources, and activities relevant to a specific project. This includes both operational QC and QA activities.
It is critical that trial managers develop a QC plan for each key operational stage of the study that defines standards against which QC will be conducted, including:
During the study design phase, QC personnel provide an independent review of the approved proposed protocol. The QC plan includes comparison of the study's CRF to the objectives set forth in the protocol to ensure that it is designed to collect all necessary data. A requirement to review CRF completion guidelines is also an element of the QC plan.
For overall site management, a complete QC plan addresses the following:
– experience in conducting clinical trials
– experience with the specific indication
– not on the FDA's restricted or debarred lists
– adequate staff and facilities
– personal involvement
– subject informed (signed informed consent form)
– subject's eligibility (inclusion/exclusion)
– protocol compliance
– adverse events (AEs) and concomitant medication
– drug accountability and storage
– medical records
– lab data
– progress notes
– diagnostic tests
– completed data clarification forms
– 21 CFR 11, 50, 54, 56, 312
– EU Clinical Trial Directives 2001/20/EC and 2005/28/EC
– ICH/GCP Consolidation Guidelines (ICH-EG).
During the data management process, the accuracy of the initial data entry is verified by an independent entry of the same data and a subsequent comparison of both sets of data for nonagreement. The reality of the data is checked with a preprogrammed logic check program and a subsequent manual review. The database entries are then QC'd versus the CRFs. The TLGs that are generated as part of a statistical analysis of the data are also inspected to ensure their accuracy, as is any text in a CSR that refers to the TLGs.
The QA activities to be conducted during a specific clinical trial are included in a QA audit plan. These activities include the number of investigator sites, selection criteria, and vendors to be audited, such as labs and drug packaging and distribution providers. This plan also specifies what internal processes of the study will be audited from initial study design, site and data management, statistical analysis, and the final CSR. It specifies audit team members and auditees for each study stage, as well as the standards against which the audit will be conducted, such as the protocol, CRF completion guidelines, SOPs, ICH/GCP guidelines, and FDA regulations.
Audits must also consider the standards of countries other than the United States, such as the recently adopted EU Clinical Trial Directives 2001/20/EC and 2005/28/EC.5
A thorough QA audit plan also clearly states the documents to be provided by the auditee, as well as the location, date, and expected duration of the audits. Preparation for QA audits should include review of the approved protocol and amendments, SOPs (both general and study-specific), any specialized training associated with the study, annotated CRFs, and the statistical analysis plan (SAP).
Internal process audits are another important QA responsibility. Internal audits review all the drug development processes employed across several studies to determine if there are systemic problems. This includes a review of employee training, compliance with SOPs and regulatory requirements, and documented evidence that QC was appropriately conducted on the output of each internal process, as well as the final deliverable to a client.
Internal audits of the site selection and management processes ensure that qualified investigators are selected, that they have adequate facilities and adequately trained staff, and that the study was conducted in compliance with the protocol and all appropriate regulations.3 Several metrics commonly evaluated by internal process audits after the study has begun include:
Computer systems validation examines all aspects of the data handling computer systems (hardware and software) to ensure the accuracy, reliability, consistent intended performance, and the ability to discern invalid or altered records. This includes initial installation and procedures that document how changes to a computer system are justified, approved, and implemented.
The validation process begins with examining user requirements, the results of the initial hardware installation qualification (IQ) tests, the operational qualification (OQ) tests, and the qualification and training of user personnel. The user acceptance test results (Performance Qualification) are then compared to the user requirements to ensure that these requirements are met. Having assurance that the data handling computer system is validated, data can then be entered.
Since an average error rate for keying text or numbers is about 1 per 300 keystrokes, the entered data is QC'd by having an independent data entry person enter the same data.2 Both sets of data are compared electronically, and discrepancies are resolved by a senior data entry person. After all of the data has been entered and all discrepancies and questions resolved, the database is QC'd by comparing the database to the CRFs from which the data was entered.
Examples of data management metrics for QA are:
Data entry and the database QC process are other critical areas of the data management process that are audited by QA personnel. The audits review the documented evidence that shows the data accuracy and integrity were verified and checked manually, independently, and programmatically to ensure the data were logical.1 These audits also ensure that all data queries are resolved and that the overall database QC review was conducted according to the QC SOP.
After a study database has undergone a QC review, it is exported into a SAS (statistical analysis system) to develop analytical programs that create data TLGs that are to be included in a CSR. The TLGs are QC'd and validated by having independent programmers create programs for the same TLGs, and all discrepancies are then resolved.
QA of the statistical analysis process ensures SAS programs are validated for the generation of all TLGs by checking that all the requirements were met and boundary conditions were tested. QA also verifies that the SAP was developed according to the processes defined in the SOPs and that all statistical analysis plans are approved by the appropriate authority.
In addition to reviewing the statistical analysis process, QA also inspects a predetermined sample of TLGs. Numbers are checked against database listings, and tables are reviewed against format requirements specified in the SAP. The QA report will document the following information:
The QA group conducts site audits throughout the course of a trial to assess protocol and regulatory compliance, to ensure that the safety and welfare of subjects are addressed, and to confirm that problems reported by study monitors have been resolved. QA's criteria for site selection include:
Site audits ensure adequate documentation of case histories (source documents), such as medical records, progress notes, hospital charts, drug accountability records, ECGs, laboratory test results, SAEs, and informed consents. Audits examine whether all clinical tests were performed at the time specified in the study protocol, and review specimen collection, storage and shipping packages (if applicable), and the timeliness of review of clinical test results.
QA site audits evaluate the timeliness of entering data into a CRF, and examine the accuracy of the data by comparing them to their respective source documents mentioned above. Audits also ensure all investigational product received by a site is adequately accounted for.
The purpose of a corrective and preventative action process is to ensure that complaints, discrepancies, and noncompliances are visible, prioritized, and tracked, and that the root cause is determined and resolved. It also provides a system to track issues of nonconformity that have not been resolved. This process requires identifying a person responsible for defining and implementing corrective action.
QA also has a critical introspective role to continually monitor and evaluate its own activities and to improve all drug development processes. This continual process of improvement tracks and reports on metrics for key activities and deliverables of drug development, keeping in mind the adage that "what gets measured, gets managed." Other inputs to process improvement include a formal debriefing after project close, client and employee satisfaction surveys, and client audits.
Managing the quality of clinical data does the following:
Martin Valania is executive director, corporate QA and compliance, with PharmaNet, 504 Carnegie Center, Princeton, NJ 08540, (609) 951-6690, fax (609) 452-5526, email: firstname.lastname@example.org.
1. I.J. Townshend and A.F. Bissel, "Sampling for Clinical Report Auditing," Statistician 36, 531–539 (1987).
2. R.K. Rondel and S.A. Varley, Clinical Data Management (John Wiley & Sons, New York, 1993).
3. U.S. Code of Federal Regulations Title 21, Part 312.
4. ICH/GCP Consolidated Guidelines, E6.
5. EU Clinical Trial Directives 2001/20/EC and 2005/28/EC.
The Author's Thoughts
The article "Quality Control and Assurance in Clinical Research" was initially written to document the extensive number of activities in both quality control (QC) and quality assurance (QA) that must be conducted to assure the accuracy and integrity of clinical trial data. The article focused on studies in which data was collected and hand written into paper case report forms (CRFs); however, the use of electronic data capture (EDC) has become more prevalent and as a result a more computer oriented set of QC skills are needed to ensure data accuracy and integrity.
Martin Valania Executive Director, Corporate QA and Compliance PharmaNet Princeton, NJ
Source data are now entered into an EDC system by site personnel who are not trained in data entry and do not have the benefit of a double data entry process. Therefore, the QC of the data which was previously done manually in a paper-based data collection system must now be done with a set of programmed edit checks that compare the entered data to a range of expected data. If the entered data is not within the pre-programmed range, an automatic query is sent to a site asking for verification of the data. In this case, the QC check has become a review of the validation of the system software that compares entered data with the range of expected data and the software program to subsequently submit an automatically generated query to a site. In this environment the QC person must be skilled in software validation techniques and processes. This is also true for a QA auditor who is responsible to review the validation process and approve the edit checks for release to production.
The importance of thoroughly conducting QC and QA when using an EDC system as well as a paper-based CRF system for collecting clinical trial data may be even more important now than it was when using paper CRFs alone for data collection because of the increased focus of regulatory agencies worldwide on the quality systems employed to ensure the accuracy and integrity of clinical trial data.
The Advisory Board's Take
This article from the March 2006 issue of Applied Clinical Trials provides an overview and summary of some of the checks and oversight procedures for a clinical trial. The focus of the article is to describe the kinds of procedures that should be employed to ensure quality data from a clinical trial.
Michael R. Hamrell President MORIAH Consultants Yorba Linda, CA
The article systematically describes all the terminology and expectations for quality steps for the conduct of a clinical trial. It goes through all the steps in the clinical trial process where quality can be applied and where a quality system program can have a great impact. There are many challenges to conducting a quality clinical trial due to all the steps involved and the variety of individuals, partners, and collaborators that all contribute to the success of a clinical trial. In the end, all of this has to come together to provide quality clinical data from a clinical study.
So why is this article so important? It is important and widely referred because it summarizes very well the contents of an overall quality program for clinical research to achieve a quality trial. The elements described then are still relevant, and given the recent FDA focus on quality systems in clinical trials, may be even more important today than it was when it was first published in 2006.