Use your browser's back button to choose another title or click here for a New Search.



How to Get the Article

 Email CTN Library (free)

PubMed Central (free)

Journal subscriber access

 

 Comments?

 

Bookmark and Share

 

From the CTN Special Issue of American Journal of Drug and Alcohol Abuse: Read the other articles here.

 

 

 

Power of Automated Algorithms for Combining Time-Line Follow-Back and Urine Drug Screening Test Results in Stimulant-Abuse Clinical Trials.

American Journal of Drug and Alcohol Abuse 2011;37(5):350-357. [doi: 10.3109/00952990.2011.601777]

Neal L. Oden, PhD (EMMES Corporation, CTN Data and Statistics Center), Paul C. VanVeldhuisen, PhD (EMMES Corporation, CTN Data and Statistics Center), Paul G. Wakim, PhD (Center for the Clinical Trials Network, NIDA), Madhukar H. Trivedi, MD (University of Texas Southwestern Medical Center, TX Node), Eugene C. Somoza, MD, PhD (University of Cincinnati/CinARC, OV Node), Daniel F. Lewis (University of Cincinnati/CinARC, OV Node).

In clinical trials of treatment for stimulant abuse, including several National Drug Abuse Treatment Clinical Trials Network (CTN) protocols, researchers commonly record both Time-Line Follow-Back (TLFB) self-reports and urine drug screen (UDS) results. This study aimed to compare the power of self-report, qualitative (use vs. no use) UDS assessment, and various algorithms to generate self-report-UDS composite measures to detect treatment differences via t-test in simulated clinical trial data. Monte Carlo simulations, patterned in part on real data to model self-report reliability, were performed on UDS errors, dropout, informatively missing UDS reports, incomplete adherence to a urine donation schedule, temporal correlation of drug use, number of days in the study period, number of patients per arm, and distribution of drug-use probabilities. Investigated algorithms include maximum likelihood and Bayesian estimates, self-report alone, UDS alone, and several simple modifications of self-report (referred to here as ELCON algorithms) which eliminate perceived contradictions between it and UDS. Among the algorithms investigated, simple ELCON algorithms gave rise to the most powerful t-tests to detect mean group differences in stimulant drug use.

Conclusions: Further investigation is needed to determine if simple, naïve procedures such as the ELCON algorithms are optimal for comparing clinical study treatment arms. But researchers who currently require an automated algorithm in scenarios similar to those simulated for combining TLFB and UDS to test group differences in stimulant use should consider one of the ELCON algorithms. This analysis continues a line of inquiry which could determine how best to measure outpatient stimulant use in clinical trials. (Article (Peer-Reviewed), PDF, English, 2011)

Keywords: CTN protocol development | Outcomes evaluation | Research design | Screening and assessment instruments | Stimulant abuse | Statistical analysis | Stimulant abuse | Timeline Follow-Back (TLFB) | Urinalysis | Urine Drug Screen (UDS) | American Journal of Drug and Alcohol Abuse (journal)

Document No: 738, PMID: 21854277, PMCID: PMC3457805.

Submitted by CTN Dissemination Librarians, 8/23/2011.

 

AUTHORS SEARCH LINK
Lewis, Daniel F. mail
Oden, Neal L. mail
Somoza, Eugene C. mail
Trivedi, Madhukar H. mail
VanVeldhuisen, Paul C. mail
Wakim, Paul G. mail


dark blue line
Supported by a grant from the National Institute on Drug Abuse to the University of Washington Alcohol and Drug Abuse Institute.
The materials on this site have neither been created nor reviewed by NIDA.
Updated 8/2011 -- http://ctndisseminationlibrary.org/display/738.htm
info@ctndisseminationlibrary.org
dark blue line