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Mathematics
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Advances in Biostatistics and Applications
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Advances in Biostatistics and Applications

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4976

Special Issue Editor

Prof. Dr. José Antonio Roldán-Nofuentes

E-Mail Website
Guest Editor
Department of Statistics (Biostatistics), School of Medicine, University of Granada, 18016 Granada, Spain
Interests: biostatistics; categorical data analysis; computational statistics; missing data in medical research; statistics in diagnostic medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The resolution of problems typical of medicine, pharmacology, biology, and health sciences in general has experienced significant progress for several decades. Biostatistics use statistical methods to solve problems typical of these areas of knowledge. The purpose of this Special Issue is to provide a collection of high-quality manuscripts on all aspects of theoretical research and novel applications of biostatistics. Simple applications to medical data are not the subject of this Special Issue.

Topics of interest include, but are not limited to, the following: Bayesian methods in biostatistics, clinical trials, diagnostic studies, epidemiology and general biostatistics, lifetime data analysis, and novel applications in health sciences.

Prof. Dr. José Antonio Roldán-Nofuentes
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 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

  • Bayesian biostatistics
  • biomarkers
  • categorical data analysis in health studies
  • clinical trials
  • diagnostic tests and roc curves
  • disease modelling
  • epidemiology
  • general biostatistics
  • lifetime data analysis in health studies
  • medical statistics
  • meta-analysis
  • pharmaceutical statistics
  • sampling methods in health studies
  • survival analysis

Published Papers (7 papers)

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Research

14 pages, 392 KiB  
Article
A Modified Cure Rate Model Based on a Piecewise Distribution with Application to Lobular Carcinoma Data
by Yolanda M. Gómez, John L. Santibañez, Vinicius F. Calsavara, Héctor W. Gómez and Diego I. Gallardo
Mathematics 2024, 12(6), 883; https://doi.org/10.3390/math12060883 - 17 Mar 2024
Viewed by 513
Abstract
A novel cure rate model is introduced by considering, for the number of concurrent causes, the modified power series distribution and, for the time to event, the recently proposed power piecewise exponential distribution. This model includes a wide variety of cure rate models, [...] Read more.
A novel cure rate model is introduced by considering, for the number of concurrent causes, the modified power series distribution and, for the time to event, the recently proposed power piecewise exponential distribution. This model includes a wide variety of cure rate models, such as binomial, Poisson, negative binomial, Haight, Borel, logarithmic, and restricted generalized Poisson. Some characteristics of the model are examined, and the estimation of parameters is performed using the Expectation–Maximization algorithm. A simulation study is presented to evaluate the performance of the estimators in finite samples. Finally, an application in a real medical dataset from a population-based study of incident cases of lobular carcinoma diagnosed in the state of São Paulo, Brazil, illustrates the advantages of the proposed model compared to other common cure rate models in the literature, particularly regarding the underestimation of the cure rate in other proposals and the improved precision in estimating the cure rate of our proposal. Full article
(This article belongs to the Special Issue Advances in Biostatistics and Applications)
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21 pages, 629 KiB  
Article
Nonparametric Additive Regression for High-Dimensional Group Testing Data
by Xinlei Zuo, Juan Ding, Junjian Zhang and Wenjun Xiong
Mathematics 2024, 12(5), 686; https://doi.org/10.3390/math12050686 - 27 Feb 2024
Viewed by 533
Abstract
Group testing has been verified as a cost-effective and time-efficient approach, where the individual samples are pooled with a predefined group size for subsequent testing. Recent research has explored the integration of covariate information to improve the modeling of the group testing data. [...] Read more.
Group testing has been verified as a cost-effective and time-efficient approach, where the individual samples are pooled with a predefined group size for subsequent testing. Recent research has explored the integration of covariate information to improve the modeling of the group testing data. While existing works for high-dimensional data primarily focus on parametric models, this study considers a more flexible generalized nonparametric additive model. Nonlinear components are approximated using B-splines and model estimation under the sparsity assumption is derived employing group lasso. Theoretical results demonstrate that our method selects the true model with a high probability and provides consistent estimates. Numerical studies are conducted to illustrate the good performance of our proposed method, using both simulated and real data. Full article
(This article belongs to the Special Issue Advances in Biostatistics and Applications)
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16 pages, 8645 KiB  
Article
Sensitivity Analysis on Hyperprior Distribution of the Variance Components of Hierarchical Bayesian Spatiotemporal Disease Mapping
by I Gede Nyoman Mindra Jaya, Farah Kristiani, Yudhie Andriyana and Anna Chadidjah
Mathematics 2024, 12(3), 451; https://doi.org/10.3390/math12030451 - 31 Jan 2024
Cited by 1 | Viewed by 645
Abstract
Spatiotemporal disease mapping modeling with count data is gaining increasing prominence. This approach serves as a benchmark in developing early warning systems for diverse disease types. Spatiotemporal modeling, characterized by its inherent complexity, integrates spatial and temporal dependency structures, as well as interactions [...] Read more.
Spatiotemporal disease mapping modeling with count data is gaining increasing prominence. This approach serves as a benchmark in developing early warning systems for diverse disease types. Spatiotemporal modeling, characterized by its inherent complexity, integrates spatial and temporal dependency structures, as well as interactions between space and time. A Bayesian approach employing a hierarchical structure serves as a solution for spatial model inference, addressing the identifiability problem often encountered when utilizing classical approaches like the maximum likelihood method. However, the hierarchical Bayesian approach faces a significant challenge in determining the hyperprior distribution for the variance components of hierarchical Bayesian spatiotemporal models. Commonly used distributions include logGamma for log inverse variance, Half-Cauchy, Penalized Complexity, and Uniform distribution for hyperparameter standard deviation. While the logGamma approach is relatively straightforward with faster computing times, it is highly sensitive to changes in hyperparameter values, specifically scale and shape. This research aims to identify the most optimal hyperprior distribution and its parameters under various conditions of spatial and temporal autocorrelation, as well as observation units, through a Monte Carlo study. Real data on dengue cases in West Java are utilized alongside simulation results. The findings indicate that, across different conditions, the Uniform hyperprior distribution proves to be the optimal choice. Full article
(This article belongs to the Special Issue Advances in Biostatistics and Applications)
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25 pages, 2278 KiB  
Article
Classification Methods for the Serological Status Based on Mixtures of Skew-Normal and Skew-t Distributions
by Tiago Dias-Domingues, Helena Mouriño and Nuno Sepúlveda
Mathematics 2024, 12(2), 217; https://doi.org/10.3390/math12020217 - 09 Jan 2024
Cited by 1 | Viewed by 560
Abstract
Gaussian mixture models are widely employed in serological data analysis to discern between seropositive and seronegative individuals. However, serological populations often exhibit significant skewness, making symmetric distributions like Normal or Student-t distributions unreliable. In this study, we propose finite mixture models based on [...] Read more.
Gaussian mixture models are widely employed in serological data analysis to discern between seropositive and seronegative individuals. However, serological populations often exhibit significant skewness, making symmetric distributions like Normal or Student-t distributions unreliable. In this study, we propose finite mixture models based on Skew-Normal and Skew-t distributions for serological data analysis. Although these distributions are well established in the literature, their application to serological data needs further exploration, with emphasis on the determination of the threshold that distinguishes seronegative from seropositive populations. Our previous work proposed three methods to estimate the cutoff point when the true serological status is unknown. This paper aims to compare the three cutoff techniques in terms of their reliability to estimate the true threshold value. To attain this goal, we conducted a Monte Carlo simulation study. The proposed cutoff points were also applied to an antibody dataset against four SARS-CoV-2 virus antigens where the true serological status is known. For this real dataset, we also compared the performance of our estimated cutoff points with the ROC curve method, commonly used in situations where the true serological status is known. Full article
(This article belongs to the Special Issue Advances in Biostatistics and Applications)
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24 pages, 1698 KiB  
Article
Hypothesis Test to Compare Two Paired Binomial Proportions: Assessment of 24 Methods
by José Antonio Roldán-Nofuentes, Tulsi Sagar Sheth and José Fernando Vera-Vera
Mathematics 2024, 12(2), 190; https://doi.org/10.3390/math12020190 - 06 Jan 2024
Viewed by 767
Abstract
The comparison of two paired binomial proportions is a topic of interest in statistics, with important applications in medicine. There are different methods in the statistical literature to solve this problem, and the McNemar test is the best known of all of them. [...] Read more.
The comparison of two paired binomial proportions is a topic of interest in statistics, with important applications in medicine. There are different methods in the statistical literature to solve this problem, and the McNemar test is the best known of all of them. The problem has been solved from a conditioned perspective, only considering the discordant pairs, and from an unconditioned perspective, considering all of the observed values. This manuscript reviews the existing methods to solve the hypothesis test of equality for the two paired proportions and proposes new methods. Monte Carlo simulation methods were carried out to study the asymptotic behaviour of the methods studied, giving some general rules of application depending on the sample size. In general terms, the Wald test, the likelihood-ratio test, and two tests based on association measures in 2 × 2 tables can always be applied, whatever the sample size is, and if the sample size is large, then the McNemar test without a continuity correction and the modified Wald test can also be applied. The results have been applied to a real example on the diagnosis of coronary heart disease. Full article
(This article belongs to the Special Issue Advances in Biostatistics and Applications)
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14 pages, 2027 KiB  
Article
Statistical Study Design for Analyzing Multiple Gene Loci Correlation in DNA Sequences
by Pianpool Kamoljitprapa, Fazil M. Baksh, Andrea De Gaetano, Orathai Polsen and Piyachat Leelasilapasart
Mathematics 2023, 11(23), 4710; https://doi.org/10.3390/math11234710 - 21 Nov 2023
Viewed by 709
Abstract
This study presents a novel statistical and computational approach using nonparametric regression, which capitalizes on correlation structure to deal with the high-dimensional data often found in pharmacogenomics, for instance, in Crohn’s inflammatory bowel disease. The empirical correlation between the test statistics, investigated via [...] Read more.
This study presents a novel statistical and computational approach using nonparametric regression, which capitalizes on correlation structure to deal with the high-dimensional data often found in pharmacogenomics, for instance, in Crohn’s inflammatory bowel disease. The empirical correlation between the test statistics, investigated via simulation, can be used as an estimate of noise. The theoretical distribution of −log10(p-value) is used to support the estimation of that optimal bandwidth for the model, which adequately controls type I error rates while maintaining reasonable power. Two proposed approaches, involving normal and Laplace-LD kernels, were evaluated by conducting a case-control study using real data from a genome-wide association study on Crohn’s disease. The study successfully identified single nucleotide polymorphisms on the NOD2 gene associated with the disease. The proposed method reduces the computational burden by approximately 33% with reasonable power, allowing for a more efficient and accurate analysis of genetic variants influencing drug responses. The study contributes to the advancement of statistical methodology for analyzing complex genetic data and is of practical advantage for the development of personalized medicine. Full article
(This article belongs to the Special Issue Advances in Biostatistics and Applications)
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20 pages, 3295 KiB  
Article
Adaptive Multiple Testing Procedure for Clinical Trials with Urn Allocation
by Hanan Hammouri, Mohammed Ali, Marwan Alquran, Areen Alquran, Ruwa Abdel Muhsen and Belal Alomari
Mathematics 2023, 11(18), 3965; https://doi.org/10.3390/math11183965 - 18 Sep 2023
Viewed by 754
Abstract
This work combines the Urn allocation and O’Brien and Fleming multiple testing procedure to compare two treatments in clinical trials in a novel way. It is shown that this approach overcomes the constraints that previously made it challenging to apply the original adaptive [...] Read more.
This work combines the Urn allocation and O’Brien and Fleming multiple testing procedure to compare two treatments in clinical trials in a novel way. It is shown that this approach overcomes the constraints that previously made it challenging to apply the original adaptive design to clinical trials. The method provides unique flexibility, enabling trials to be stopped early if one treatment shows it is superior without compromising the efficiency of the original multiple testing procedure in terms of type I error rate and power. Experimental data and simulated case examples are used to illustrate the efficacy and robustness of this original approach and its potential for usage in a variety of clinical settings. Full article
(This article belongs to the Special Issue Advances in Biostatistics and Applications)
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