Buckman, JEJ;
Cohen, ZD;
O'Driscoll, C;
Fried, EI;
Saunders, R;
Ambler, G;
DeRubeis, R;
... Pilling, S; + view all
(2020)
Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches.
OSF Preprints: Charlottesville, VA, USA.
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Abstract
Aims: To develop, validate, and compare the performance of nine models predicting post-treatment outcomes for depressed adults based on pre-treatment data. / Methods: Individual patient data from all six eligible RCTs were used to develop (k=3, n=1722) and test (k=3, n=1136) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum-scores were developed using coefficient weights derived from network centrality statistics (Models 1-3) and factor loadings from a confirmatory factor analysis (Model 4). Unweighted sum-score models were tested using Elastic Net Regularized (ENR) and ordinary least squares (OLS) regression (Models 5-6). Individual items were then included in ENR and OLS (Models 7-8). All models were compared to one another and to a null model using the mean post-baseline BDI-II score in the training data (Model 9). Primary outcome: BDI-II scores at 3-4 months. / Results: Models 1-7 all outperformed the null model. Individual-item models (particularly Model 8) explained less variance. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum-scores had little impact. / Conclusions: Any of the modelling techniques (1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.
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