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Harmonizing Depression Measures Across Studies: a Tutorial for Data Harmonization
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AI
DOI：10.1007/s11121-022-01381-5.png)
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Abstract
En 中文
There has been increasing interest in applying integrative data analysis (IDA) to analyze data across multiple studies to increase sample size and statistical power. Measures of a construct are frequently not consistent across studies. This article provides a tutorial on the complex decisions that occur when conducting harmonization of measures for an IDA, including item selection, response coding, and modeling decisions. We analyzed caregivers' self-reported data from the ADHD Teen Integrative Data Analysis Longitudinal (ADHD TIDAL) dataset; data from 621 of 854 caregivers were available. We used moderated nonlinear factor analysis (MNLFA) to harmonize items reflecting depressive symptoms. Items were drawn from the Symptom Checklist 90-Revised, the Patient Health Questionnaire-9, and the World Health Organization Quality of Life questionnaire. Conducting IDA often requires more programming skills (e.g., Mplus), statistical knowledge (e.g., IRT framework), and complex decision-making processes than single-study analyses and meta-analyses. Through this paper, we described how we evaluated item characteristics, determined differences across studies, and created a single harmonized factor score that can be used to analyze data across all four studies. We also presented our questions, challenges, and decision-making processes; for example, we explained the thought process and course of actions when models did not converge. This tutorial provides a resource to support prevention scientists to generate harmonized variables accounting for sample and study differences.
Keywords:
Depression
Harmonized measure
Item response theory (IRT)
Integrative data analysis (IDA)