Identifying relapse predictors in individual participant data with decision trees.

ABSTRACT:

Background

Depression is a highly common and recurrent condition. Predicting who is at most risk of relapse or recurrence can inform clinical practice. Applying machine-learning methods to Individual Participant Data (IPD) can be promising to improve the accuracy of risk predictions.

Methods

Individual data of four Randomized Controlled Trials (RCTs) evaluating antidepressant treatment compared to psychological interventions with tapering ([Formula: see text]) were used to identify predictors of relapse and/or recurrence. Ten baseline predictors were assessed. Decision trees with and without gradient boosting were applied. To study the robustness of decision-tree classifications, we also performed a complementary logistic regression analysis.

Results

The combination o

SUBMITTER: Bottcher L 

PROVIDER: S-EPMC10644580 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature