Empirically contrasting urine drug screening-based opioid use disorder treatment outcome definitions

Area of Science:

• Addiction Medicine
• Clinical Research Methodology
• Data Science in Healthcare

Background:

• Lack of consensus on optimal outcome measures for opioid use disorder (OUD) treatment efficacy hinders research synthesis.
• Existing definitions vary in precise computation and application, complicating meta-analyses.

Purpose of the Study:

• To empirically contrast multiple clinical trial definitions of treatment success for OUD.
• To apply these definitions to a harmonized dataset from large-scale clinical trials.

Main Methods:

• Developed an R software package to operationalize 61 treatment outcome definitions based on urine drug screening (UDS) results.
• Applied these definitions to a harmonized dataset from 2492 participants across three National Drug Abuse Treatment Clinical Trials Network (CTN) studies testing medication for OUD (MOUD).
• Utilized hierarchical clustering to empirically contrast the outcome definitions.

Main Results:

• Identified three distinct clusters of outcome definitions.
• Cluster 1 (8 definitions) focused on detecting opioid-positive UDS without including missing data, potentially inflating success rates.
• Cluster 2 (10 definitions) had strict criteria, relying heavily on UDS from short periods or single visits.
• Cluster 3 (43 definitions) offered diverse measures of abstinence, use reduction, and relapse, providing more balanced assessments.

Conclusions:

• Clinical trials should incorporate missing UDS data into outcome computations.
• Avoid over-reliance on UDS data limited to short time frames or single positive tests after abstinence.
• More balanced outcome measures are needed for accurate OUD treatment efficacy assessment.