Prediction Modeling

Uses

Evidence quality

Other

Highlighted claims

• The study used adaptive LASSO logistic regression to identify predictors. — Predicting depressive and anxiety symptoms among Lebanese and Syrian adults in a suburb of Beirut, Lebanon, during concurrent crises: nested cross-sectional study
• The LASSO penalty parameter was selected through 10-fold cross-validation. — Predicting depressive and anxiety symptoms among Lebanese and Syrian adults in a suburb of Beirut, Lebanon, during concurrent crises: nested cross-sectional study
• Separate adjusted logistic regressions were run on selected predictors to provide interpretable odds ratios and confidence intervals. — Predicting depressive and anxiety symptoms among Lebanese and Syrian adults in a suburb of Beirut, Lebanon, during concurrent crises: nested cross-sectional study
• Model performance was assessed using discrimination and calibration metrics including AUC, calibration slope, calibration-in-the-large, expected-to-observed ratio, and calibration plots. — Predicting depressive and anxiety symptoms among Lebanese and Syrian adults in a suburb of Beirut, Lebanon, during concurrent crises: nested cross-sectional study
• The models require external validation before broader use beyond the studied setting. — Predicting depressive and anxiety symptoms among Lebanese and Syrian adults in a suburb of Beirut, Lebanon, during concurrent crises: nested cross-sectional study
• The Lebanese depression model performed well with AUC 0.81 and near-perfect expected-to-observed ratio. — Predicting depressive and anxiety symptoms among Lebanese and Syrian adults in a suburb of Beirut, Lebanon, during concurrent crises: nested cross-sectional study
• The models may support targeted screening, triage, outreach, resource allocation, service prioritisation, and public health planning. — Predicting depressive and anxiety symptoms among Lebanese and Syrian adults in a suburb of Beirut, Lebanon, during concurrent crises: nested cross-sectional study