Journal article
A Randomized Comparison of Medication and Cognitive Behavioral Therapy for Treating Depression in Low-Income Young Minority Women
Medical science monitor, Vol.22, pp.4947-4953
12/16/2016
DOI: 10.12659/MSM.902206
PMCID: PMC5189608
PMID: 27981956
Abstract
BACKGROUND Longitudinal data arise frequently in biomedical science and health studies where each subject is repeatedly measured over time. We compared the effectiveness of medication and cognitive behavioral therapy on depression in predominantly low-income young minority women. MATERIAL AND METHODS The treatment effects on patients with low-level depression may differ from the treatment effects on patients with high-level depression. We used a quantile regression model for longitudinal data analysis to determine which treatment is most beneficial for patients at different stress levels over time. RESULTS The results confirm that both treatments are effective in reducing the depression score over time, regardless of the depression level. CONCLUSIONS Compared to cognitive behavioral therapy, treatment with medication more often effective, although the size of the effect differs. Thus, no matter how severe a patient's depression symptoms are, antidepressant medication is effective in decreasing depression symptoms.
Details
- Title: Subtitle
- A Randomized Comparison of Medication and Cognitive Behavioral Therapy for Treating Depression in Low-Income Young Minority Women
- Creators
- Hyunkeun Cho - Department of Statistics, Western Michigan University, Kalamazoo, MI, USASang Joon Son - Department of Psychiatry, Ajou University School of Medicine, Suwon, South KoreaSanghee Kim - Department of Statistics, Western Michigan University, Kalamazoo, MI, USAJungsik Park - Center for Medical Humanities and Convergent Content, Ajou University, Suwon, South Korea
- Resource Type
- Journal article
- Publication Details
- Medical science monitor, Vol.22, pp.4947-4953
- DOI
- 10.12659/MSM.902206
- PMID
- 27981956
- PMCID
- PMC5189608
- NLM abbreviation
- Med Sci Monit
- ISSN
- 1234-1010
- eISSN
- 1643-3750
- Publisher
- United States
- Language
- English
- Date published
- 12/16/2016
- Academic Unit
- Biostatistics
- Record Identifier
- 9983997348802771
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