ArticleFrontiers in psychiatry2025
A case study on using quantile regression in psychiatry research.
Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.