Advances in Statistics, Biostatistics and Medical Statistics

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1150

Special Issue Editor


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Guest Editor
Department of Statistics and Operations Research, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
Interests: statistics; biostatistical methods; applied biostatistics; serological data; mixture models

Special Issue Information

Dear Colleagues,

The fields of statistics, biostatistics, and medical statistics are experiencing rapid advancements driven by the increasing complexity of data and the critical need for rigorous analytical methods in health-related research. This Special Issue brings together a collection of cutting-edge research articles that showcase the latest developments in these domains. The contributions highlight innovative statistical methodologies, novel applications in biostatistics, and transformative approaches in medical statistics. Topics covered include, but are not limited to, advanced modeling techniques, high-dimensional data analysis, machine learning integration, and the statistical challenges posed by personalized medicine. The Special Issue aims to serve as a valuable resource for statisticians, biostatisticians, and medical researchers, providing insights into emerging trends and offering practical solutions to current statistical challenges in the biomedical and public health sectors. By fostering interdisciplinary collaboration and knowledge exchange, this Special Issue contributes to the ongoing evolution of statistical science in the context of medical and health-related research.

Dr. Tiago Dias Domingues
Guest Editor

Manuscript Submission Information

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Keywords

  • biostatistics
  • epidemiology
  • statistical methods applied to medicine
  • serological data analysis
  • statistical inference

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Published Papers (1 paper)

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Research

16 pages, 531 KiB  
Article
Improved Mixture Cure Model Using Machine Learning Approaches
by Huina Wang, Tian Feng and Baosheng Liang
Mathematics 2025, 13(4), 557; https://doi.org/10.3390/math13040557 - 8 Feb 2025
Viewed by 692
Abstract
The mixture cure model has been widely used in medicine, public health, and bioinformatics. The traditional mixture cure model has limitations in model flexibility and handling complex structured data and big data. In recent years, some improved new methods have been developed. Through [...] Read more.
The mixture cure model has been widely used in medicine, public health, and bioinformatics. The traditional mixture cure model has limitations in model flexibility and handling complex structured data and big data. In recent years, some improved new methods have been developed. Through a literature review and numerical studies, this article discusses the advantages and disadvantages of the progressions of mixture cure models incorporating machine learning techniques such as SVMs for model improvements. Machine learning algorithms have advantages in model flexibility and computation. When combined with mixture cure models, they can effectively improve the performance of mixture cure models, distinguish between susceptible and non-susceptible individuals, and accurately predict the influencing factors and their magnitude of incidence and latency. Full article
(This article belongs to the Special Issue Advances in Statistics, Biostatistics and Medical Statistics)
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