Predictive model of the treatment effect for patients with major depressive disorder

Journal Article

The model to evaluate and predict the effectiveness of treatment of the Major Depressive Disorder (MDD) was developed and estimated using MindLinc data. The clinical global impression (CGI) scale with seven categories was used to measure the patient's state. The proportional odds model was selected because of ordinal nature of the outcome. The set of predictors included i) CGI score measured at preceded visit, ii) three groups of medications (antidepressants, atypical medicine, and augmentation medicine), all categorized for appropriate number of strata (from six to nine) and their daily doses, iii) psychiatric comorbidities, iv) type of the therapy used (talk vs. medications), v) demographic variables (e.g., age group, sex), and vi) the history of the efficiency of prior treatment. More than a half of a million records with measured CGI scores and their predictors were identified in the MindLinc database and used for model estimation. The predicted model of future CGI scales was developed and evaluated for single and recurrent episodes of MDD. Significant estimates were obtained for demographic factors, history of previous SGI scales, and for comorbidity and treatment indices. The methods of causal inferences based on the inverse probability weighting approach were applied to evaluate the treatment effects. The model extensions allowing for addressing the limitations of the proportional odds model are discussed. Copyright © 2007 by the Association for Computing Machinery.

Full Text

Duke Authors

Cited Authors

  • Akushevich, I; Kravchenko, J; Gersing, K; Mane, KK

Published Date

  • November 28, 2013

Published In

  • 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013

Start / End Page

  • 518 - 524

Digital Object Identifier (DOI)

  • 10.1145/2506583.2506618

Citation Source

  • Scopus