Data cleaning and harmonization of clinical trial data: Medication-assisted treatment for opioid use disorder.

Several large-scale, pragmatic clinical trials on opioid use disorder (OUD) have been completed in the National Drug Abuse Treatment Clinical Trials Network (CTN). However, the resulting data have not been harmonized between the studies to compare the patient characteristics. This paper provides lessons learned from a large-scale harmonization process that are critical for all biomedical researchers collecting new data and those tasked with combining datasets.

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

Background And Aims: A lack of consensus on the optimal outcome measures to assess opioid use disorder (OUD) treatment efficacy and their precise definition and computation has hampered the pooling of research data for evidence synthesis and meta-analyses. This study aimed to empirically contrast multiple clinical trial definitions of treatment success by applying them to the same dataset.

Methods: Data analysis used a suite of functions, developed as a software package for the R language, to operationalize 61 treatment outcome definitions based on urine drug screening (UDS) results.

Individual-Level Risk Prediction of Return to Use During Opioid Use Disorder Treatment.

Importance: No existing model allows clinicians to predict whether patients might return to opioid use in the early stages of treatment for opioid use disorder.

Objective: To develop an individual-level prediction tool for risk of return to use in opioid use disorder.

Design, Setting, And Participants: This decision analytical model used predictive modeling with individual-level data harmonized in June 1, 2019, to October 1, 2022, from 3 multicenter, pragmatic, randomized clinical trials of at least 12 weeks' duration within the National Institute on Drug Abuse Clinical Trials Network (CTN) performed between 2006 and 2016.

Biomarkers for Duchenne muscular dystrophy progression: impact of age in the mdx tongue spared muscle.

Background: Duchenne muscular dystrophy (DMD) is a severe form of muscular dystrophy without an effective treatment, caused by mutations in the DMD gene, leading to the absence of dystrophin. DMD results in muscle weakness, loss of ambulation, and death at an early age. Metabolomics studies in mdx mice, the most used model for DMD, reveal changes in metabolites associated with muscle degeneration and aging.

Capturing drug use patterns at a glance: An n-ary word sufficient statistic for repeated univariate categorical values.

Introduction: The efficacy of treatments for substance use disorders (SUD) is tested in clinical trials in which participants typically provide urine samples to detect whether the person has used certain substances via urine drug screenings (UDS). UDS data form the foundation of treatment outcome assessment in the vast majority of SUD clinical trials. However, existing methods to calculate treatment outcomes are not standardized, impeding comparability between studies and prohibiting reproducibility of results.