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Abstract

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.

Introduction

The ultimate goal of clinical data management (CDM) is to complete every study with a dataset that accurately represents data captured in the study. No matter how much care is taken in collecting and entering data, discrepancies and data errors will invariably find their way into a clinical database. The vast majority of these data inconsistencies and errors can be alleviated with careful review and data-cleaning activities.

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Carefully designed edit checks can greatly increase efficiency and data quality by automating many data review processes within the clinical database or clinical data management system (CDMS). CDM personnel and members of the study team should collaborate to determine what edit checks should be in place to fulfill study requirements and reduce potential data errors and inconsistencies. Although assignment of responsibilities varies between organizations, CDM may be involved with all phases of edit check specification and testing, with the possible exception of edit check programming.

Scope

This chapter discusses the use of edit checks in clinical studies, including the purpose of edit checks, types of edit checks, creation processes of edit check specifications and development, and edit check testing. The chapter is intended as an overview of edit checks from a CDM perspective, and does not discuss details of programming and conditional statements used in edit checks.

Roles and responsibilities vary between organizations, and some of the topics discussed in this chapter may be the responsibility of different departments in different organizations. Regardless of role assignment, CDM personnel should be aware of the processes discussed in this chapter and how they impact their roles as data managers.

Minimum Standards

• Finalize protocol and complete initial database specifications prior to designing edit checks.

• Specify edit checks based on parameters of case report form (CRF) pages and safety and efficacy parameters from the protocol.

• Specify edit checks for all primary study endpoints and safety data.

• If applicable, specify edit checks with external data (e.g., laboratory data) for reconciliation purposes.

• Ensure all edit checks are programmed, validated, and documented in accordance with established standard operating procedures.

• Ensure all edit checks specification documents are appropriately version controlled.

• Provide training to relevant personnel on the impact of edit checks on their individual roles in entering and managing clinical data.

Best Practices

• Where appropriate, specify edit checks to compare study inclusion and exclusion criteria and any data (that are collected in CRF pages) that could be indicative of protocol violations.

• Design edit check specifications so redundant output does not occur when edit checks are run.

• Review edit checks with appropriate clinical and statistical personnel to ensure edit checks meet study needs and help identify inconsistencies in study endpoints.

• Specify edit checks for all study endpoints and all data supporting safety data and study endpoints.

• Develop a library of standard CRFs and edit checks based on standards used, such as CDASH or company-specific standards.

• Perform a quality control review of edit check design and specifications prior to performing user acceptance testing (UAT) of edit checks.

• Evaluate the effectiveness of edit checks once in active use, and modify, delete or create new edit checks accordingly.

Purpose and Process of Edit Checks

The purpose of edit checks is to draw attention to data that are inconsistent or potentially erroneous. Edit checks may be described as automatic warnings or notices that are generated by a database, CDMS, or other data entry application, and are triggered by data that are missing, out of range, unexpected, redundant, incompatible or otherwise discrepant with other data or study parameters. Most edit checks are triggered during the data entry process, and may prompt the data entry operator to double check a value before saving the data. Other edit checks may be triggered by characteristics of related or aggregate data, and are more likely to notify CDM personnel of potential data errors after data entry has occurred. The potential data errors identified by triggered edit checks may prompt CDM personnel to perform data-cleaning activities such as performing self-evident corrections or generating queries to a site.

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Table 1. Sample Edit Check Specification Table

Hierarchical View of Edit Checks

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• Study, site, and subject or subject record—While adhering to data privacy conventions and regulations, queries and edit check outputs should clearly identify the study, site, and subject record for which an edit check or query is triggered.

• Variable name and value—Queries and edit check outputs should clearly identify what field, variable and value triggered the edit check or query.

• Supporting values—If an edit check or query is triggered from a derived value or is associated with other fields, supporting values should also be identified. For example, if an edit check is triggered by an out-of-range value for computed body mass index, the output message should indicate the value’s relationship to the supporting fields containing subject height and weight.

• Message composition—Queries and edit check output messages should clearly identify the discrepant data and acceptable options for discrepancy resolution, but should not introduce bias or pose leading questions in any way. For example, an edit check for blood pressure should not output a message that specifies the expected range. Rather, the message should simply state that the value is out of the expected range and request confirmation or correction of the blood pressure.

Types of Checks

Edit checks are created to identify a number of different types of data inconsistencies or potential data errors. Although most edit checks are programmed into the database or CDMS and are triggered automatically when predefined conditions are met, data inconsistencies and potential data errors may also be found through manual data review.

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• Checks of external data - Programmed edit checks are not limited to CRF data, but may also be applied to external data (lab data, ECG data, etc.). Many of these types of checks are primarily designed to help ensure that external data are consistent with the subject data within the database.

• Protocol violations - These checks are designed to identify specific data that may be indicative of protocol violations, and may take the form of range checks. One example would be calculating date ranges for follow-up visits to ensure all follow-up visits were within protocol-specified time windows. Another example would be checking subject eligibility forms to ensure all inclusion criteria were met and no exclusion criteria were met.

Front-End vs. Back-End Edit Checks

Edit checks that are triggered upon data entry are often referred to as front-end edit checks, whereas edit checks across multiple forms are often known as back-end edit checks. Front-end edit checks are typically limited to a single field or CRF page. An example of a front-end edit check would be a flag or warning that appears when an entry operator attempts to enter an impossible visit date, such as February 30 or a date in the future. Although front-end edit checks are usually more numerous, back-end edit checks are typically more complicated and therefore more difficult to program. An example of a back- end edit check would be one that notifies CDM personnel that a BMI (body mass index) entry is not consistent with the subject’s reported height and weight.

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Table 2. Comparison of Edit Check Types

Electronic Data Capture (EDC) vs. Paper-based Edit Checks

Edit checks used in paper-based studies may differ somewhat from those used in EDC studies. For paper-based studies, some organizations may choose to limit the number of front-end checks. This ensures that potentially critical errors or discrepancies will be addressed directly by qualified CDM personnel. For studies using EDC, checks for transcription errors are not as necessary. However, more care must be taken in EDC studies to ensure the data entry design and front-end edit checks catch potential errors as they are entered. Because the electronic record may be considered the source document in some situations, there may be no other documentation to check against if possible errors are discovered later. The potential lack of additional source documentation in EDC studies also increases the importance of ensuring all edit checks are in place prior to the start of data collection. For more details about edit checks in studies using EDC, see the GCDMP chapter entitled “Electronic Data Capture—Concepts and Study Start-up.”

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• Quality control (QC)—Although QC responsibilities may vary between organizations, some form of QC should be performed for the entire edit check validation process, final edit check programming, and all associated documentation. In different organizations, some or all of these QC processes may fall under the responsibilities of CDM personnel, project managers, database programmers, quality assurance personnel, or a manager of database development.

• Validation of new or revised edit checks—If any edit checks are added or revised during the course of a study, the same steps should be followed as are used for edit checks created at the beginning of the study.

Maintenance of Edit Checks

After edit check testing and validation has been completed, all responsible parties should provide written approval of edit check documentation prior to using the edit checks with actual subject data. CDM typically maintains an edit check document, ensuring that the document is kept current and incorporates proper version or change control. If substantial changes are made to the edit check document or the study is ongoing for more than a year, prior to study closeout CDM may request an additional review and approval of the final edit check document or changes made to the document. This re-review is intended to ensure that the needs of all parties continue to be met.

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Upon conclusion of a study, the final version of the edit check document should be archived with all other pertinent study documentation.

Edit Check Training

All data entry and CDM personnel who will be entering data, reviewing data, or reviewing the output of edit checks should be trained prior to data entry into the database. All personnel involved with these processes should have basic training in the formats, terminology, and use of edit checks, and the documentation of this training should reside in training folders. Training can be tailored to each individual role. For example, data entry personnel may only be trained on those edit checks that may be triggered upon data entry.

Data entry and CDM personnel may also need to undergo study-specific training for any edit checks that are unusual or unique to the study. If needed, a brief overview of the study and a review of the CRF may be included in the training. Study-specific training should also have clear documentation, and may be maintained in training folders if confidentiality is not a concern. Otherwise, documentation of study-specific training may be maintained by data management and archived with all other pertinent study documentation at the close of the study.

Recommended Standard Operating Procedures

• Database Design
• Edit Check Specifications
• Edit Check Validation 

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