Event Summary:
A one-day workshop organized by the Center for Interdisciplinary Research and
Consulting at the University of Maryland, Baltimore County (UMBC), and the
Center for Innovative Study Design (CISD) at Stanford University in the field of Clinical Trials and Regulatory Science.
Registration is now closed
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Organizing Committee:
Faculty from UMBC and Stanford University and leaders from FDA and industry.
For more information:
Please contact Dr. Yi Huang, Department of Mathematics and
Statistics, University of Maryland Baltimore County (UMBC),
1000 Hilltop Circle, Baltimore, MD 21250.
E-Mail: yihuang@umbc.edu.
For any Technical difficulties Please contact Zana Coulibaly at czana1@umbc.edu.
Participant Information
Gaurav Sharma from The Emmes Corporation.
Paper: Analysis of a Drug Abuse Intervention Trial
In clinical trials, a common way to collect illicit drug use information is self report, yet accuracy of self reported drug use is highly controversial. Some studies have shown good of self report with biological measures of drug use, while others have shown poor concordance. The reliability and validity of self report are limited by the veracity and recall ability of research participants. In this talk, analysis using data from the National Drug Abuse Treatment Clinical Trials Network (NIDA CTN) randomized trial "Screening, Motivational Assessment, Referral, and Treatment in Emergency Departments (CTN-0047; SMART-ED)" will be presented. Behavioral patterns of daily self-reported drug use over a one-year period among participants enrolled in the study will be evaluated. Self-reported drug use during follow-up over a 90-day recall period on the Timeline Follow Back Instrument will be compared to the biological measure of drug use from hair analysis. Measures of disagreement, including under reporting (self report negative when biological measures drug use) and over reporting (self report positive when biological measure does not indicate drug use) will be presented. Predictors of agreement between hair analysis and self-report of drug use will be examined and an attempt to build a model to predict drug use will be developed.