Data Quality Webinar Series

Webinar #1 Defining and Measuring Data Quality: Basic Practices
Webinar #2 Advanced Approaches to Data Quality

Overview

Aligning Data Quality practices with the Critical Path risk-based philosophies: new approaches for Data Managers to measure and control Data Quality, including statistical process control and trend analyses.

Presenters

Kit Howard, MS, CCDM, Principal and Owner, Kestrel Consultants
Meredith Nahm, MS CQA CCDM, Associate Director, Biomedical Informatics, Duke Translational Medicine Institute

Target Audience

Higher experience level - Data Managers, Managers of Data Groups, Quality Control and Quality Assurance Statisticians, Project Managers

Learning Outcomes - Webinar #1

At the conclusion of this webinar, participants should be able to:

Define Data Quality (fitness for use, dimensions of data quality, and uses of data)
Define an error
Describe the relationship between data cleaning and data quality.
Identify ways in which data errors impact clinical trials (variability, analysis, which errors impact analysis, Statistical Process Control)
Identify approaches to trend recognition and handling

Learning Outcomes - Webinar #2

At the conclusion of this webinar, participants should be able to:

Describe the data handling process, tools and data management plans
Identify sources of error
Describe how to perform database audits and generate reports per guidelines
Identify the root cause of data problems
Describe and list corrective action responses
Describe how to achieve data quality from protocol through study report using an integrated cross-functional approach

CDM Competencies addressed in the Data Quality Webinar Series

Understands the data handling process, tools and data management plan.
Understands and applies quality acceptance criteria.
Distinguishes critical from non-critical errors.
Performs database audit and generates report per guidelines.
Documents database QC performed.
Develops and maintains process and standard SOPs relevant to database audits.
Establishes quality criteria and quality error rates acceptance limits.
Evaluates data points that should be audited.
Establishes timelines to perform an audit and oversees activities of audit team.
Identifies root cause of errors in order to recommend change in process/technology to assure the error doesn't occur again (continuous improvement).
Ensures adequate preparation for internal and external database and data management audits, can respond knowledgeably and confidently to issues or questions that arise during audits.
Ensures scope of audits include all potential sources of data errors.
Ensures that sampling audit techniques are statistically valid and support decisions made about data.
Applies sound judgment on handling audit findings.
Identifies inconsistent data points.
Understands algorithms for derivations, collapsing, and imputation.
Defines requirements for data listings.
Identifies trends in data.
Identifies outliers.
Deleted from capabilities. What does this mean?
Handles and documents outliers.

References/ List of Resources

Good Clinical Data Management Practices, (March 2007), Society for Clinical Data Management: Milwaukee WI.

Davis, Jonathan R., Nolan, Vivian P, Woodcock, Janet, Estabrook, Ronald W. eds. (1999). Assuring data quality and validity in clinical trials for regulatory decision making workshop report. Division of Health Sciences Policy, Institute of Medicine. National Academy Press: Washington, DC.

FDA's Investigations Operations Manual 2008. Retrieved on April 24, 2008 from http://www.fda.gov/ora/inspect_ref/iom/default.htm

Goldfarb, Norman. (November 2006). Are site monitoring and data cleaning a waste of time? Journal of Clinical Research Best Practices. V.2 N.11.

Woollen, Stan. (2003). Misconduct in research: innocent ignorance or malicious malfeasance. Retrieved on April 24, 2008 from www.fda.gov/oc/gcp/slideshows/2003/gcp2003.ppt.

GCDMP Chapters

Measuring Data Quality
Assuring Data Quality

Continuing Education Units

SCDM will award continuing education units to participants who attend the entire webinar, successfully pass the 5 assessment questions (60%) and complete the evaluation forms.
CEUs available: .20 CEUs for each webinar - 1 contact hour = .1 CEU.