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This is the data and code for a submitted manuscript. Abstract: While Major Depressive Disorder (MDD) is a leading cause of disability, prior investigations may have been limited by studying single explanatory factors rather than considering multiple etiologies simultaneously. The current study used elastic net regularized regression and predictors from sociodemographic, self-report, polygenic scores, resting electroencephalography, pupillometry, actigraphy, and cognitive tasks to classify 217 individuals into currently depressed (MDD), psychiatric control (PC), and no current psychopathology (NP) groups, as well as predicting symptom severity and lifetime MDD. Cross-validated models explained as much as 65.7% of the variance in depression symptom severity and as little as 0.4% of the deviance between PC and NP groups. Predictor importance varied across models; however, psychosocial functioning, self-referent processing, rumination, and self-reported sleep quality emerged across several models. Findings highlight the advantages of using psychiatric control groups to isolate factors specific to MDD and the etiologic complexity associated with the maintenance of depression.
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Sep 12, 2019
McNamara, Molly, 2019, "Replication Data for: Multifactorial Prediction of Depression Diagnosis and Symptom Dimensions", https://doi.org/10.18738/T8/DEFPNZ, Texas Data Repository Dataverse, V1
Data and code for a submitted manuscript. Abstract: While Major Depressive Disorder (MDD) is a leading cause of disability, prior investigations may have been limited by studying single explanatory factors rather than considering multiple etiologies simultaneously. The current st...
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