ArticlePloS one2026
Unveiling the Multifaceted Dynamics of Breast Cancer: A Copula Regression Approach to Modeling and Predicting Outcomes.
Article in PloS one, 2026. 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
This study examines the application of flexible copula regression models to analyze the complex interdependencies among clinical variables in breast cancer data. As the most commonly diagnosed cancer and the second leading cause of cancer-related deaths among women worldwide, breast cancer presents both clinical and analytical challenges. Unlike traditional multivariate approaches, copulas offer greater flexibility in capturing complex, nonlinear, and asymmetric dependencies between mixed-type outcomes. The present study examines copula-based regression models to investigate the joint behavior of clinical variables in patients with breast cancer. We explored multiple copula families to jointly model overall survival (binary) and age at diagnosis (continuous) in the METABRIC dataset. Goodness-of-fit metrics guide model comparison and selection, with the Gumbel copula demonstrating superior performance in capturing the upper tail dependence associated with favorable outcomes, such as younger age and improved survival. Formal model comparison against an independent margins baseline confirmed that accounting for dependence via a copula significantly improves model fit (likelihood ratio test: [Formula: see text], df = 1, p < 0.0001), and PIT diagnostics validated the adequacy of both marginal specifications. The findings support the integration of copula models into clinical research, facilitating a more nuanced understanding of cancer progression and enabling more accurate risk assessment and data-driven decision-making in oncology.
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.