Topic Editors

1. Faculty of Business and Administration, University of Bucharest, 030018 Bucharest, Romania
2. National Institute of Research and Development for Biological Sciences, Splaiul Independenței 296, 060031 Bucharest, Romania
Dr. Adrian Pană
Faculty of Business and Administration, University of Bucharest, 030018 Bucharest, Romania
Prof. Dr. Cǎtǎlina Liliana Andrei
Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila" Bucharest, 050474 Bucharest, Romania

Application of Biostatistics in Medical Sciences and Global Health

Abstract submission deadline
31 August 2026
Manuscript submission deadline
31 October 2026
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587

Topic Information

Dear Colleagues,

We are pleased to invite you to contribute to our Topic, "Application of Biostatistics in Medical Sciences and Global Health", which aims to showcase recent advancements in statistical modeling, machine learning, and artificial intelligence (AI) in medical research. This Topic focuses on innovative biostatistical methodologies that enhance disease burden estimation, improve diagnostic accuracy, and optimize treatment strategies.

The integration of biostatistics and machine learning has transformed how we study and combat diseases. Advanced statistical models are crucial for accurately measuring the burden of diseases across populations, particularly for cardiovascular diseases, diabetes, and cancer. Machine learning and AI have proven valuable for predictive modeling, risk stratification, and personalized treatment strategies.

A special emphasis is placed on Raman spectroscopy in cancer research. This technique has shown promise in distinguishing cancer subtypes based on spectral data, offering a highly accurate method for early cancer detection. Studies integrating Raman spectroscopy with AI and statistical modeling are highly encouraged.

In addition, antimicrobial resistance (AMR) poses a growing global health challenge. We welcome research applying statistical modeling and machine learning to study AMR trends, predict resistance patterns, and develop targeted interventions.

We encourage contributions on the following topics:

  • Statistical modeling of disease burden and epidemiological trend;
  • Machine learning and AI applications in cardiology, diabetes, and cancer;
  • Predictive analytics for treatment response and patient outcomes;
  • Raman spectroscopy in cancer classification;
  • Computational and statistical approaches to antimicrobial resistance.

We look forward to receiving your submissions and advancing the role of biostatistics in global health.

Prof. Dr. Bogdan Oancea
Dr. Adrian Pană
Prof. Dr. Cǎtǎlina Liliana Andrei
Topic Editors

Keywords

  • statistical modeling of disease burden
  • machine learning in medical sciences
  • AI in cardiology, diabetes, and cancer
  • Raman spectroscopy in cancer classification
  • computational approaches to antimicrobial resistance

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Journal of Personalized Medicine
jpm
- 6.0 2011 21.5 Days CHF 2600 Submit
Mathematics
mathematics
2.2 4.6 2013 18.4 Days CHF 2600 Submit
Applied Sciences
applsci
2.5 5.5 2011 19.8 Days CHF 2400 Submit
Stats
stats
1.0 1.8 2018 18.2 Days CHF 1600 Submit
Healthcare
healthcare
2.7 4.7 2013 21.5 Days CHF 2700 Submit

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Published Papers (1 paper)

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25 pages, 374 KiB  
Article
Goodness-of-Fit Tests for Combined Unilateral and Bilateral Data
by Jia Zhou and Chang-Xing Ma
Mathematics 2025, 13(15), 2501; https://doi.org/10.3390/math13152501 - 3 Aug 2025
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Abstract
Clinical trials involving paired organs often yield a mixture of unilateral and bilateral data, where each subject may contribute either one or two responses. While unilateral responses from different individuals can be treated as independent, bilateral responses from the same individual are likely [...] Read more.
Clinical trials involving paired organs often yield a mixture of unilateral and bilateral data, where each subject may contribute either one or two responses. While unilateral responses from different individuals can be treated as independent, bilateral responses from the same individual are likely correlated. Various statistical methods have been developed to account for this intra-subject correlation in the bilateral data, and in practice, it is crucial to select a model that properly accounts for this correlation to ensure accurate inference. Previous research has investigated goodness-of-fit test statistics for correlated bilateral data under different group settings, assuming fully observed paired outcomes. In this work, we extend these methods to the more general and practically common setting where unilateral and bilateral data are combined. We examine the performance of various goodness-of-fit statistics under different statistical models, including the Clayton copula model. Simulation results indicate that the performance of the goodness-of-fit tests is model-dependent, especially when the sample size is small and/or the intra-subject correlation is high. However, the three bootstrap methods generally offer more robust performance. In real world applications from otolaryngologic and ophthalmologic studies, model choice significantly impacts conclusions, emphasizing the need for appropriate model assessment in practice. Full article
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