Association networks in a matched case-control design – Co-occurrence patterns of preexisting chronic medical conditions in patients with major depression versus their matched controls

Min hyung Kim, Samprit Banerjee, Yize Zhao, Fei Wang, Yiye Zhang, Yongjun Zhu, Joseph DeFerio, Lauren Evans, Sang Min Park, Jyotishman Pathak

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Objective: We present a method for comparing association networks in a matched case-control design, which provides a high-level comparison of co-occurrence patterns of features after adjusting for confounding factors. We demonstrate this approach by examining the differential distribution of chronic medical conditions in patients with major depressive disorder (MDD) compared to the distribution of these conditions in their matched controls. Materials and methods: Newly diagnosed MDD patients were matched to controls based on their demographic characteristics, socioeconomic status, place of residence, and healthcare service utilization in the Korean National Health Insurance Service's National Sample Cohort. Differences in the networks of chronic medical conditions in newly diagnosed MDD cases treated with antidepressants, and their matched controls, were prioritized with a permutation test accounting for the false discovery rate. Sensitivity analyses for the associations between prioritized pairs of chronic medical conditions and new MDD diagnosis were performed with regression modeling. Results: By comparing the association networks of chronic medical conditions in newly diagnosed depression patients and their matched controls, five pairs of such conditions were prioritized among 105 possible pairs after controlling the false discovery rate at 5%. In sensitivity analyses using regression modeling, four out of the five prioritized pairs were statistically significant for the interaction terms. Conclusion: Association networks in a matched case-control design can provide a high-level comparison of comorbid features after adjusting for confounding factors, thereby supplementing traditional clinical study approaches. We demonstrate the differential co-occurrence pattern of chronic medical conditions in patients with MDD and prioritize the chronic conditions that have statistically significant interactions in regression models for depression.

Original languageEnglish
Pages (from-to)88-95
Number of pages8
JournalJournal of Biomedical Informatics
Volume87
DOIs
Publication statusPublished - 2018 Nov

Bibliographical note

Funding Information:
Funding: This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [ 2017R1D1A1B03033721 ]; United States National Institutes of Health (NIH) [ R01MH105384 ]; [UL1TR000457-06]; [P50MH113838] and [T32MH019132].

Publisher Copyright:
© 2018

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

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