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 updated June 30, 2005

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Analysis to Improve Reduction in Crack Use (B-Start)

Project Title	Analysis to Improve Reduction in Crack Use (B-Start)
Funding Source	NIH, NIDA
Project Dates	08/01/1999 - 07/31/2000
Project Number	1 R03 DA12900-01
Team	Principal Investigators- Ty A. Ridenour, Ph.D. Co-Investigator- Linda B. Cottler, Ph.D. Wilson Compton, III, M.D. Edward Spitznagel, Ph.D.

Abstract

This project is investigating the use of an innovative analysis to improve outcomes and efficient delivery of interventions to reduce crack use. As part of NIDA’s Cooperative Agreement for AIDS Community-Based Outreach, St. Louis crack users were randomly assigned to either: NIDA’s standard educational intervention or the EachOneTeachOne (EOTO) enhanced education/counseling, peer-led intervention, designed and written in previous research conducted by the Epidemiology and Prevention Group at Washington University. The interventions reduced the overall sample’s mean number of days of crack use in a month by 5 days (outcomes) from baseline to the three month follow-up. However, regression analysis of NIDA/EOTO data revealed that different types of crack users were helped most by either the NIDA intervention or the EOTO intervention (an attribute-treatment interaction; ATI). If future crack users were assigned to either the NIDA or EOTO intervention based on which intervention was predicted to yield the best outcome for each individual, the number of days per month that crack was consumed would decrease from baseline to follow-up by 6.2 days- a 24% improvement in outcomes compared to randomly assigned interventions.

It has been argued, however, that regression techniques do not provide enough statistical power to detect ATIs. Indeed, several participant attributes correlated nearly significantly (p=.06 to .08) with outcomes in one intervention and not the other, so a more powerful analytical technique should improve the precision of predicting crack users’ intervention outcomes. Not only could interventions be assigned to individuals with greater precision because of the more accurate estimates of future crack users’ outcomes, reduction in crack use might be greater. One analytical technique, artificial neural network (ANN) analysis (used by engineers and economists), has evidenced better specificity and sensitivity than clinicians’ diagnoses and regression techniques for medical diagnoses and outcomes. ANN may be more powerful than regression because ANN: assumes no particular data distribution (e.g., bell-shaped vs dichotomous), accounts for high-order interactions among variables without a-priori specification, and accounts for multicolinearity. In this study, ANN will be compared to linear regression in terms of their a) degree of error in predicting observed outcomes, b) power to detect ATIs, and c) clinical utility for ATI research. This study will produce data regarding which intervention is more effective for reducing crack use among different types of crack users. The study’s implications are far-reaching as ATI research has been used in many treatment, educational, and industrial settings.
 

 

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© 2004 Washington University in St. Louis