A case study on using quantile regression in psychiatry research.

Commonly used linear regression focuses only on the effect on the mean value of the dependent variable and may not be useful in situations where relationships across the distribution are of interest. This study aimed to appraise the utility of Quantile Regression (QR), a technique that can model any quantile value of the dependent variable. The primary aim of this study is to provide an overview of the QR method and its practical applications in psychiatric research. We demonstrated this with an exploratory analysis of the data on neuropsychological test performance among 119 subjects with obsessive-compulsive disorder (OCD). The varying effects of age, education, sex, antipsychotic use, and symptom severity between extreme quantiles were highlighted using simple and multiple QR models. While linear regression is easy to employ and interpret, QR is not only on par in performance but also more flexible in identifying a set of factors that may differ depending on the quantile of interest. QR analysis is a potent tool in applications where the effect of the independent variable varies depending on the values of the outcome variable. The results of this exploratory study suggest that the QR approach could potentially help explore inconsistent findings, generate future hypotheses, and/or provide possible interpretive frameworks for inconsistencies observed in neuropsychological research in OCD. As the QR offers a complete distributional analysis, it is valuable in providing new insights, especially in situations where the usual regression assumptions are violated or when interested the in extreme values of the outcome of interest.