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Mathematics
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Advances in Statistics, Biostatistics and Medical Statistics
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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 5660

Special Issue Editor

Dr. Tiago Dias Domingues

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

29 pages, 1420 KB  
Article
Application of Markov Models to Cost-Effectiveness Analysis in the Selection of Patients for Liver Transplantation
by Hugo Pereira, Raquel J. Fonseca and Helena Mouriño
Mathematics 2025, 13(22), 3683; https://doi.org/10.3390/math13223683 - 17 Nov 2025
Viewed by 482
Abstract
Background: Liver transplantation is the most effective curative treatment for patients with hepatocellular carcinoma. Due to the scarcity of cadaveric donor livers, several selection criteria have been established; however, these criteria are highly restrictive. In this study, we compare alternative selection tools with [...] Read more.
Background: Liver transplantation is the most effective curative treatment for patients with hepatocellular carcinoma. Due to the scarcity of cadaveric donor livers, several selection criteria have been established; however, these criteria are highly restrictive. In this study, we compare alternative selection tools with the standard selection criterion, the Milan Criteria. We conducted a cost-effectiveness analysis from the perspective of the U.S. healthcare system to determine which criterion provides the greatest benefit to the health system. Methods: An innovative non-homogeneous Markov model was developed to simulate the health trajectories of patients with hepatocellular carcinoma who underwent liver transplantation over five years. The model incorporated time-dependent transition probabilities, enabling the simulation to capture the evolving risks of recurrence and mortality. Transition probabilities, costs, and QALYs were obtained from published studies, while recurrence probabilities were estimated using the Kaplan–Meier method based on a cohort of 149 patients. We evaluated mean recurrence-free survival, life years gained, quality of life, and the incremental cost-effectiveness ratio (ICER) relative to the Milan Criteria. Results: HepatoPredict yielded the most significant benefits but incurred higher total costs than the other criteria. The ICERs of HepatoPredict Class I and Class II relative to the MC were $14,689.58/QALY and $39,542.98/QALY, respectively. Both values were below the cost-effectiveness threshold (U.S. GDP per capita: $81,632.25/QALY), indicating that HepatoPredict is cost-effective in the U.S. healthcare system. Conclusions: HepatoPredict stands out as the most cost-effective criterion and optimises organ allocation, an especially important consideration given the scarcity of donor livers. This represents a substantial advantage for healthcare institutions. Full article
(This article belongs to the Special Issue Advances in Statistics, Biostatistics and Medical Statistics)
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23 pages, 593 KB  
Article
Enhancing Postpartum Haemorrhage Prediction Through the Integration of Classical Logistic Regression and Machine Learning Algorithms
by Muriel Lérias-Cambeiro, Raquel Mugeiro-Silva, Anabela Rodrigues, Tiago Dias-Domingues, Filipa Lança and António Vaz Carneiro
Mathematics 2025, 13(21), 3376; https://doi.org/10.3390/math13213376 - 23 Oct 2025
Viewed by 690
Abstract
Postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. The early identification of bleeding risk in individual women is crucial for enabling timely interventions and improving patient outcomes.This study aims to evaluate various exploratory and classification methodologies, alongside [...] Read more.
Postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. The early identification of bleeding risk in individual women is crucial for enabling timely interventions and improving patient outcomes.This study aims to evaluate various exploratory and classification methodologies, alongside optimisation strategies, for identifying predictors of postpartum haemorrhage. K-means clustering was employed on a retrospective cohort of patients, incorporating demographic, obstetric, and laboratory variables, to delineate patient profiles and select pertinent features. Initially, a classical logistic regression model, implemented without cross-validation, facilitated the identification of six significant predictors for postpartum haemorrhage: lactate dehydrogenase, urea, platelet count, non-O blood group, gestational age, and first-degree lacerations, all of which are variables routinely available in clinical practice. Furthermore, machine learning algorithms—including stepwise logistic regression, ridge logistic regression, and random forest—were utilised, applying cross-validation to optimise predictive performance and enhance generalisability. Among these methodologies, ridge logistic regression emerged as the most effective model, achieving the following metrics: sensitivity 0.857, specificity 0.875, accuracy 0.871, F1-score 0.759, and AUC 0.907. While machine learning techniques demonstrated superior performance, the integration of classical statistical methods with machine learning approaches provides a robust framework for generating reliable predictions and fostering significant clinical insights. Full article
(This article belongs to the Special Issue Advances in Statistics, Biostatistics and Medical Statistics)
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16 pages, 856 KB  
Article
Comparison of Parametric Rate Models for Gap Times Between Recurrent Events
by Ivo Sousa-Ferreira, Ana Maria Abreu and Cristina Rocha
Mathematics 2025, 13(12), 1931; https://doi.org/10.3390/math13121931 - 10 Jun 2025
Viewed by 882
Abstract
Over the past two decades, substantial efforts have been made to develop survival models for gap times between recurrent events. An emerging approach involves considering rate models derived from a non-homogeneous Poisson process, thus allowing the conditional distribution of a gap time given [...] Read more.
Over the past two decades, substantial efforts have been made to develop survival models for gap times between recurrent events. An emerging approach involves considering rate models derived from a non-homogeneous Poisson process, thus allowing the conditional distribution of a gap time given the previous recurrence time to be deduced. Under this approach, some parametric rate models have been proposed, differing in their distributional assumptions on gap times. In particular, the extended exponential–Poisson, Weibull and extended Chen–Poisson distributions have been considered. Alternatively, a flexible rate model using restricted cubic splines is proposed here to capture complex non-monotonic rate shapes. Moreover, a comprehensive comparison of parametric rate models is presented. The maximum likelihood method is applied for parameter estimation in the presence of right-censoring. It is shown that some models include important special cases that allow testing of the independence assumption between a gap time and the previous recurrence time. The likelihood ratio test, as well as two information criteria, are discussed for model selection. Model fit is assessed using Cox–Snell residuals. Applications to two well-known clinical data sets illustrate the comparative performance of both the existing and proposed models, as well as their practical relevance. Full article
(This article belongs to the Special Issue Advances in Statistics, Biostatistics and Medical Statistics)
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16 pages, 531 KB  
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 2465
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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