Clinical trials are becoming more challenging, time-consuming and resource intensive. In addition to the pressure for timeliness which face the sites at the front lines of clinical trials, today's standard data cleaning methods cater to approaches that are at high risk of inadvertently introducing bias into the data, thus skewing the trial results.
This presentation explores the premise that the very process of data cleaning has the potential to in fact do more harm than good in more detail. The presentation also introduces predictive, proactive, and preventative real-life measures that can be deployed to manage the level of errors and the need for data cleaning, thus improving the overall data quality.
This audio conference covers:
- The inherent risks in the process of data cleaning
- How traditional cleaning methods cleaning can introduce bias and degrade data quality
- How extensive use of automated edit checks with EDC could worsen the potential for bias
- Alternatives to traditional data cleaning that flow from a philosophy of "first, do no harm"
- Managing the need for data cleaning based on proactive, primary quality driven approaches
About the speaker:
Elaine Job is the Executive Director of Clinical Data Management at Covance, Inc. She holds an advanced degree in Biometrics and has more than 20 years of accumulated clinical drug development experience, gained in assorted statistical, project, and data management roles.
She has worked in academic, government, pharmaceutical, and non-profit settings as well as her current CRO position. Elaine has been a frequent speaker at professional meetings on the topic of data standardization and is an ex-officio board member of the Clinical Data Interchange Standards Consortium (CDISC).
Who should attend?
- Operations
- Research & Development
- Regulatory Affairs
- Outsourcing
- Management
This audio conference is classified as General Interest.