Seeing the forest for the trees: Predicting attendance in trials for co-occurring PTSD and substance use disorders with a machine learning approach.

ABSTRACT: Objective: High dropout rates are common in randomized clinical trials (RCTs) for comorbid posttraumatic stress disorder and substance use disorders (PTSD + SUD). Optimizing attendance is a priority for PTSD + SUD treatment development, yet research has found few consistent associations to guide responsive strategies. In this study, we employed a data-driven pipeline for identifying salient and reliable predictors of attendance. Method: In a novel application of the iterative Random Forest algorithm (iRF), we investigated the association of individual level characteristics and session attendance in a completed RCT for PTSD + SUD (n = 70; women = 22 [31.4%]). iRF identified a group of potential predictor candidates for the total trial sessions attended; then, a P

SUBMITTER: Lopez-Castro T 

PROVIDER: S-EPMC9426719 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature