Statistical Modeling and Analysis in Medical Research

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 876

Special Issue Editors


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Guest Editor
Health Informatics Institute, College of Public Health, University of South Florida, Tampa, FL 33612, USA
Interests: statistical methods development and computation; longitudinal data analysis; survival analysis; mixed effect modeling; joint modeling of longitudinal and survival analysis; genetics and genomics; Bayesian statistics

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Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
Interests: epidemic modeling; network science
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Special Issue Information

Dear Colleagues,

“Statistical Modeling and Analysis in Medical Research” is a Special Issue under the journal of Mathematics by Multidisciplinary Digital Publishing Institute (MDPI), which is a world-renown journal. We seek to provide a forum for statisticians to share their groundbreaking findings in medical research, with important real-world applications.

We are pleased to invite you to publish your work in this statistical Special Issue. Areas of interest are those related to the development and applications of statistical methods in medical research, such as in biological and biomedical research, genetics and genomics, public health, medicine, epidemiology and infectious diseases, clinical trial design and analysis, oncology statistics, and other related medical research topics.

This Special Issue aims to publish original research that presents statistical methods motivated by real data and novel approaches on how to solve statistical challenges.

Dr. Lazarus K. Mramba
Dr. Qihui Yang
Guest Editors

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Keywords

  • statistics
  • biostatistics
  • biometrics

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Published Papers (1 paper)

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Research

27 pages, 834 KB  
Article
A Bivariate Copula–Driven Multi-State Model for Statistical Analysis in Medical Research
by Hugo Brango, Roger Tovar-Falón and Guillermo Martínez-Flórez
Mathematics 2025, 13(19), 3072; https://doi.org/10.3390/math13193072 - 24 Sep 2025
Viewed by 340
Abstract
We develop and evaluate a copula-based multistate model for illness–death processes with dependent transition times. The framework couples Cox proportional hazards models for the marginal transition intensities with Archimedean copulas to capture dependence, and it is estimated via the Inference Functions for Margins [...] Read more.
We develop and evaluate a copula-based multistate model for illness–death processes with dependent transition times. The framework couples Cox proportional hazards models for the marginal transition intensities with Archimedean copulas to capture dependence, and it is estimated via the Inference Functions for Margins (IFM) approach under right censoring. A Monte Carlo study shows that assuming independence between transitions can severely underestimate joint survival, yielding coverage as low as 40% under strong dependence, compared with 92% to 97% when copulas are used. We apply the method to a large Colombian cohort of COVID-19 patients (2021 to 2022) that includes sociodemographic, clinical, and vaccination data. The Gumbel copula best captures the strong positive dependence between hospitalization and death, producing more accurate joint survival estimates than independence-based models. Model diagnostics, including proportional hazards tests, Kaplan-Meier comparisons, hazard rate functions, and TTT plots, support the adequacy of the Cox margins. We also discuss limitations and avenues for extension, such as parametric or cure-fraction margins, nested or vine copulas, and full-likelihood estimation. Overall, the results underscore the methodological and applied value of integrating copulas into multistate models, offering a robust framework for analyzing dependent event times in epidemiology and biomedicine. Full article
(This article belongs to the Special Issue Statistical Modeling and Analysis in Medical Research)
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