Rigor Through Redundancy: Keeping It Old School

Описание: Quality assurance in HIV behavioral randomized trials requires ongoing monitoring and significant time and effort to ensure data integrity. While our methods might be considered “old school,” they consistently catch and correct errors that automated systems alone would overlook. After all, fully automated data management should yield perfectly clean data—if only humans were infallible, reliable, and predictable. To maintain the highest data quality and the best experience for participants, we invest ongoing resources in monitoring and assurance. In this presentation, we will share our approach, including key strategies, tips, and tricks, followed by an interactive discussion with the audience.

Sam Dilworth:
With over 20 years of data management and statistics experience, Samantha Dilworth, MS, is a data manager and senior statistician at the Division of Prevention Science, Center for AIDS Prevention Sciences at UCSF. Her expertise includes building complex data management collection systems for multiple studies, creating randomization schemes for R01s, cleaning and managing data across multiple projects, creating analysis-ready datasets, performing statistical analyses, and contributing to publications.

Lara Coffin:
Lara Coffin, MPH, is a public health professional with extensive experience leading NIH-funded HIV community intervention studies. She has contributed to research and program development across Santa Cruz, San Francisco, Ann Arbor, New York, and Seattle. Lara’s expertise encompasses all aspects of research coordination, including fiscal management, regulatory compliance, team supervision, data integrity, and daily operations. Committed to advancing public health, she is dedicated to initiatives that promote equitable access to care and prevention resources.

A CAPS Methods Core Town Hall.

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Recorded: Wednesday, March 26, 2025.

0:00 Introductions
3:03 Quality Assurance Goals
6:56 Standard Operating Procedures SOPs
10:34 Calendars
13:10 Sample Pipeline
15:54 Tracking Database
29:58 TBD Forms
33:20 Comparison Report Remedies
36:29 Data Discrepancies
41:06 Conclusions
42:10 Q & A