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Mathematics
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Current Research in Biostatistics, 2nd Edition
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Current Research in Biostatistics, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 10 June 2025 | Viewed by 513

Special Issue Editors

Dr. Miguel Ángel Montero-Alonso

E-Mail Website
Guest Editor
Department of Statistics and O.I., Faculty of Medicine, University of Granada, 18016 Granada, Spain
Interests: inference in diagnostic models; biostatistics; statistic elearning; data science; medical statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juan De Dios Luna del Castillo

E-Mail Website
Guest Editor
Department of Biostatistic, University of Granada, 18071 Granada, Spain
Interests: predictive models; inference in diagnostic models; biostatistics; scale validation; teaching biostatistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Building on the success of the first edition, this Special Issue continues to explore the evolving applications of biostatistics in biomedical and health sciences. As biostatistics plays a crucial role in addressing complex medical and health-related problems, this edition aims to highlight innovative methodologies and their practical applications across various fields.

We invite contributions that address topics including, but not limited to, the following:

  • Advanced diagnostic and prognostic models;
  • causal inference methodologies;
  • analysis of large-scale databases and data fusion techniques;
  • approaches to managing missing data;
  • pedagogical strategies for teaching statistics in the health sciences;
  • stochastic models applied to biology and health sciences;
  • spatio-temporal analysis of disease distribution;
  • innovative measures of association in 2x2 tables;
  • development and implementation of biostatistical software;
  • experimental design advancements;
  • insights from controlled clinical trials;
  • techniques for information fusion;
  • integration of observational data from multiple sources;
  • estimation methods for prevalence and incidence across various sampling schemes.

We welcome submissions that clearly define the problem being addressed, the methodology employed, and the solutions provided.

Dr. Miguel Ángel Montero-Alonso
Prof. Dr. Juan De Dios Luna del Castillo
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • diagnostic models
  • causal inference
  • computational biostatistics
  • measures of association
  • prognostic models

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Published Papers (1 paper)

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Research

16 pages, 265 KiB  
Article
Point and Interval Estimation of Population Prevalence Using a Fallible Test and a Non-Probabilistic Sample: Post-Stratification Correction
by Jorge Mario Estrada Alvarez, Juan de Dios Luna del Castillo and Miguel Ángel Montero-Alonso
Mathematics 2025, 13(5), 805; https://doi.org/10.3390/math13050805 - 28 Feb 2025
Viewed by 327
Abstract
Accurate prevalence estimation is crucial for public health planning, particularly for rare diseases or low-prevalence conditions. This study evaluated frequentist and Bayesian methods for estimating prevalence, addressing challenges such as imperfect diagnostic tests, partial disease status verification, and non-probabilistic samples. Post-stratification was applied [...] Read more.
Accurate prevalence estimation is crucial for public health planning, particularly for rare diseases or low-prevalence conditions. This study evaluated frequentist and Bayesian methods for estimating prevalence, addressing challenges such as imperfect diagnostic tests, partial disease status verification, and non-probabilistic samples. Post-stratification was applied as a novel method and was used to improve representativeness and correct biases. Three scenarios were analyzed: (1) complete verification using a gold standard, (2) estimation with a diagnostic test of known sensitivity and specificity, and (3) partial verification of disease status limited to test positives. In all scenarios, post-stratification adjustments increased prevalence estimates and interval lengths, highlighting the importance of accounting for population variability. Bayesian methods demonstrated advantages in integrating prior information and modeling uncertainty, particularly under high-variability and low-prevalence conditions. Key findings included the flexibility of Bayesian approaches to maintain estimates within plausible ranges and the effectiveness of post-stratification in correcting biases in non-probabilistic samples. Frequentist methods provided narrower intervals but were limited in addressing inherent uncertainties. This study underscores the need for methodological adjustments in epidemiological studies, offering robust solutions for real-world challenges. These results have significant implications for improving public health decision-making and the design of prevalence studies in resource-constrained or non-probabilistic contexts. Full article
(This article belongs to the Special Issue Current Research in Biostatistics, 2nd Edition)
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