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Harmonizing Depression Measures Across Studies: a Tutorial for Data Harmonization.

Xin Zhao1,2,3, Stefany Coxe4, Margaret H Sibley5,6,4

  • 1University of Washington School of Medicine, Seattle, WA, USA. xzhao022@fiu.edu.

Prevention Science : the Official Journal of the Society for Prevention Research
|July 7, 2022
PubMed
Summary
This summary is machine-generated.

Integrative Data Analysis (IDA) combines data from multiple studies, but inconsistent measures require harmonization. This tutorial guides researchers through harmonizing measures for more powerful cross-study analyses.

Keywords:
DepressionHarmonized measureIntegrative data analysis (IDA)Item response theory (IRT)

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Area of Science:

  • Psychology
  • Psychiatry
  • Prevention Science

Background:

  • Integrative Data Analysis (IDA) enhances statistical power by pooling data across studies.
  • Inconsistent measurement tools across studies pose a significant challenge for IDA.
  • Harmonization of measures is crucial for valid cross-study comparisons and analyses.

Purpose of the Study:

  • To provide a tutorial on the complex decision-making processes involved in harmonizing measures for IDA.
  • To demonstrate harmonization techniques using caregiver self-reported depressive symptoms data from the ADHD TIDAL dataset.
  • To offer guidance for prevention scientists on creating harmonized variables that account for sample and study variations.

Main Methods:

  • Utilized moderated nonlinear factor analysis (MNLFA) to harmonize depressive symptom items from multiple questionnaires (SCL-90-R, PHQ-9, WHOQOL).
  • Evaluated item characteristics and identified differences across studies to inform harmonization decisions.
  • Described the process of creating a single harmonized factor score for cross-study analysis.

Main Results:

  • Successfully harmonized depressive symptom measures from disparate sources into a single factor score.
  • Demonstrated a systematic approach to addressing challenges in harmonization, including model convergence issues.
  • The harmonized factor score enables robust analysis across multiple studies, accounting for measurement inconsistencies.

Conclusions:

  • Harmonization is a complex but essential step for successful IDA, requiring advanced statistical and programming skills.
  • This tutorial offers a practical framework and decision-making guide for researchers conducting measure harmonization.
  • The presented methods facilitate the generation of reliable, harmonized variables for cross-study research in prevention science.