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A case study on using quantile regression in psychiatry research.
Ravi G Shankar1, Thennarasu Kandavel1, Himani Kashyap2
1Department of Biostatistics, National Institute of Mental Health and Neuro Sciences, Bengaluru, Karnataka, India.
Frontiers in Psychiatry
|January 21, 2026
Summary
Quantile Regression (QR) offers a flexible alternative to standard regression, modeling the entire outcome distribution. This method reveals how factors impact different quantiles, proving valuable in psychiatric research for exploring complex relationships.
Area of Science:
- Psychiatry
- Statistics
- Neuroscience
Background:
- Linear regression models only the mean, limiting analysis when relationships vary across the dependent variable's distribution.
- Understanding these varying relationships is crucial in psychiatric research, particularly in neuropsychological assessments.
Purpose of the Study:
- To evaluate the utility of Quantile Regression (QR) in psychiatric research.
- To provide an overview of QR methodology and its practical applications.
- To explore neuropsychological test performance in obsessive-compulsive disorder (OCD) using QR.
Main Methods:
- An overview of Quantile Regression (QR) principles.
- Exploratory analysis of neuropsychological test data from 119 subjects with OCD.
- Application of simple and multiple QR models to examine factors across outcome quantiles.
Main Results:
- Quantile Regression (QR) demonstrated varying effects of age, education, sex, antipsychotic use, and symptom severity on different quantiles of neuropsychological test performance.
- QR identified factors influencing extreme quantiles, offering insights beyond mean-level analysis.
- The study highlighted QR's flexibility compared to linear regression in capturing quantile-specific effects.
Conclusions:
- Quantile Regression (QR) is a potent tool for psychiatric research, especially when the impact of independent variables differs across the outcome distribution.
- QR can help reconcile inconsistent findings and generate new hypotheses in neuropsychological research, particularly in conditions like OCD.
- The method provides valuable distributional insights, useful when standard regression assumptions are violated or extreme values are of interest.
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