Using Machine Learning to Identify and Investigate Moderators of Alcohol Use Intervention Effects in Meta-Analyses.

ABSTRACT:

Aims

To illustrate a machine learning-based approach for identifying and investigating moderators of alcohol use intervention effects in aggregate-data meta-analysis.

Methods

We illustrated the machine learning technique of random forest modeling using data from an ongoing meta-analysis of brief substance use interventions implemented in general healthcare settings. A subset of 40 trials testing brief alcohol interventions (BAIs) was used; these trials provided 344 estimates of post-intervention effects on participants' alcohol use as well as data on 20 potential moderators of intervention effects. These candidate moderators included characteristics of trial methodology and implementation, intervention design and participant samples.

Results

The best-fitting random fo

SUBMITTER: Parr NJ 

PROVIDER: S-EPMC8753777 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature